volas 2.0.0

High-performance, Rust-backed columnar kernel for stock / candlestick (OHLCV) time-series data.

volas

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High-performance, Rust-backed columnar kernel for stock / candlestick (OHLCV) time-series data.

volas is a Rust-backed, pandas-shaped DataFrame for live OHLCV pipelines: 254 trading-indicators, incremental O(lookback) refresh, and NumPy/Torch-ready output.

It is not a general-purpose pandas replacement. It is a narrow, fast DataFrame for candlestick / OHLCV workflows: append a new bar, keep indicator columns cached, and refresh only the stale tail.

volas is also a Rust crate.

from volas import read_csv

df = read_csv("btc_1m.csv")

# Cache indicator directives as DataFrame columns.
df["rsi:14"]
df[["macd", "macd.signal", "atr:14"]]

# In a live loop:
df.append(new_bar)     # one-row OHLCV frame
df["rsi:14"]           # refreshes only the affected tail, O(lookback)
features = df.to_numpy()

• 254 built-in indicators and TA-Lib-compatible directives
• Incremental refresh after append: O(lookback), not O(n)
• Rust kernels, no pandas runtime dependency
• pandas-shaped indexing: .loc / .iloc / .at / read_csv / to_numpy
• NumPy / Torch-ready output

pip install volas

On our reproducible benchmark suite, volas is faster than pandas, polars, stock-pandas and TA-Lib on most live-update indicator workloads.

Why volas

• pandas-shaped API. The same .loc / .iloc / .at, read_csv, to_numpy and resampling — for OHLCV workflows, change the import and keep your code. It is not a general-purpose pandas replacement. (See what's not covered)
• Fast on live OHLCV indicator workloads, with reproducible benchmarks — see the always-current live benchmark report.
  • On the current published report, volas beats TA-Lib on 139 / 157 covered indicators by the default ratio — reproducible via make benchmark.
  • On incremental update (each new bar), volas is the fastest of every library across all indicators — ~5× faster than TA-Lib, and up to ~360× faster than pandas.
• Built for the live tick. A new bar touches only the affected tail (O(lookback), not O(n)); indicators refresh in microseconds, never a full recompute.
• Rust inside, NumPy / Torch out. Compiled kernels — hot paths tuned down to the assembly-instruction level — zero pandas at runtime; to_numpy() feeds NumPy and torch.Tensor pipelines.

Why I built volas

I've spent years building quantitative trading-signal systems, and for most of that time pandas was just the tax I paid to get any work done. Loading a single CSV of a few hundred thousand candlesticks, computing a handful of indicators, cleaning them up, normalizing — that one data-processing pass routinely took 10 to 20 minutes before any real research could even begin.

Backtesting made it worse. To honestly simulate live trading you feed history in one bar at a time — usually fine-grained bars, say 1-minute candles — appending each new bar to the DataFrame and recomputing every indicator across the whole frame again, bar after bar, to mimic the OHLCV stream a live system actually sees. So much of that was pure waste — the same columns rebuilt from scratch on every step — and across a few years of 1-minute data that redundant work alone could drag a single backtest out by hours. Every idea I wanted to try, every parameter I wanted to sweep, paid that tax again. The tooling, not the thinking, was setting the pace of my research.

So I stopped patching around it and rebuilt the whole data layer from the ground up, pandas thrown out entirely. The bet paid off: the data-processing pass that used to take 10–20 minutes now finishes in seconds, and the per-bar recomputation that used to drag those runs out collapsed to near nothing. A backtest still has real work to do — strategy logic, fills, accounting — but the data layer stopped being the thing I sit and wait on. volas is that layer: hundreds of times faster where it counts, and finally fast enough to get out of the way.

When to reach for volas

volas is not a general-purpose pandas replacement — for plain dataframe analysis, keep pandas or polars. It is a narrow, fast DataFrame for the case where a new OHLCV bar arrives and indicators must refresh now:

	pandas	polars	TA-Lib	volas
pandas-shaped indexing (.loc / .iloc / .at)	✅	❌	❌	✅
OHLCV-native indicator directives (df['rsi:14'])	❌	❌	✅	✅
Indicator cache owned by the frame	❌	❌	❌	✅
Incremental O(lookback) refresh on a new bar	❌	❌	❌	✅
Rust-backed kernels, no pandas at runtime	❌	✅	C	✅
NumPy / Torch export	✅	✅	arrays	✅

Table of Content

• Installation
• Quick start
• Usage
• Bounded rolling window
• Cumulation and DatetimeIndex
• TimeFrame
• Syntax of directive
• Indexing & selection
• Writing & assignment
• Timezones
• Missing values (volas.NA)
• pandas interop
• Arrow & DLPack interop (zero-copy)
• Error handling
• Built-in Indicators
• License
• For Developers

Installation

pip install volas

Requires Python >= 3.11. Wheels are published for Linux (x86_64 / aarch64), macOS (x86_64 / arm64) and Windows (x86_64). For a local build from source, see For Developers.

Verify the install in 30 seconds, then see the examples/ — each is self-contained and prints an OK: line:

pip install volas
python examples/00_install_check.py
python examples/03_live_ohlcv_append.py   # append a bar, refresh only the stale tail

Quick start

from volas import DataFrame

df = DataFrame({
    'open':   [2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
    'high':   [12.0, 13.0, 14.0, 15.0, 16.0, 17.0],
    'low':    [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
    'close':  [3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
    'volume': [100, 200, 300, 400, 500, 600],
})

# A plain column -> Series
df['close']
# 0    3.0
# 1    4.0
# 2    5.0
# 3    6.0
# 4    7.0
# 5    8.0
# Name: close, dtype: float64

# An indicator directive -> Series (2-period SMA of `close`)
df['ma:2']
# 0   <NA>
# 1    3.5
# 2    4.5
# 3    5.5
# 4    6.5
# 5    7.5
# Name: ma:2, dtype: float64

# A boolean directive -> bool Series, usable as a row mask
bullish = df['close > open']
df[bullish]                      # DataFrame of the rows where close > open

# Several directives at once -> DataFrame
df[['ma:2', 'ma:3', 'close > open']]

# Export to NumPy (and, zero-copy, to Arrow / DLPack — see the interop section)
df['close'].to_numpy()           # 1-D ndarray
df.to_numpy()                    # 2-D ndarray (rows x columns)

Usage

from volas import (
    DataFrame, Series, read_csv, to_datetime, TimeFrame, Timestamp,
)

The sub-sections below follow volas's public surface in order: the DataFrame class, then its instance methods, its static methods, the other classes, and the top-level package functions — closing with the rest of the pandas-compatible API that behaves exactly as it does in pandas. (A top-level name imported from volas, such as read_csv, is written without a volas. prefix.)

DataFrame(data, columns=None, time_frame=None, cumulators=None)

DataFrame has a pandas-compatible API, so if you are familiar with pandas.DataFrame, you are already ready to use volas. Unlike pandas, volas is backed by a Rust kernel and has no pandas runtime dependency.

df = read_csv('stock.csv')

We can use [], which is called pandas indexing (a.k.a. __getitem__ in python) to select out lower-dimensional slices. In addition to indexing with colname (the column name of the DataFrame), we could also do indexing by directives.

df[directive]                  # Gets a Series

df[[directive0, directive1]]   # Gets a DataFrame

We have an example to show the most basic indexing using [directive]

df = DataFrame({
    'open' : ...,
    'high' : ...,
    'low'  : ...,
    'close': [5, 6, 7, 8, 9]
})

df['ma:2']

# 0   <NA>
# 1    5.5
# 2    6.5
# 3    7.5
# 4    8.5
# Name: ma:2, dtype: float64

Which gets the 2-period simple moving average on column "close".

Parameters

• data dict[str, list | np.ndarray] | DataFrame the column data, one of:

  • a dict mapping each column name to an equal-length list or NumPy array (float, int, bool, datetime64, or string);
  • another volas DataFrame, which is then copied (like pandas.DataFrame(df)).

  The constructor does not accept a pandas.DataFrame or an Arrow object — bridge those with the dedicated DataFrame.from_pandas / DataFrame.from_arrow instead. To attach a DatetimeIndex, parse a column with to_datetime, promote it with set_index, then tag a zone with tz_localize / tz_convert. See Timezones.

• columns Optional[list[str]] = None Select and order the columns to keep — the same projection as df[[...]]. A name not present raises KeyError; an empty list or a duplicate name is rejected, and an absent column is never silently filled.

• time_frame Optional[str | TimeFrame] = None If set, makes this a tf-aware (cumulating) DataFrame at this bar interval: the given rows are taken as already-final bars at that frame, and later appends fold finer bars into the forming bar. Requires a DatetimeIndex. See Cumulation and DatetimeIndex.

• cumulators Optional[dict[str, str]] = None Per-column aggregator overrides used when folding (e.g. {'amount': 'sum'}), only meaningful together with time_frame. Defaults to OHLCV semantics (open=first, high=max, low=min, close=last, volume=sum; any other column last). Each dict value is one of:

  • 'first' — the first value in the bucket
  • 'last' — the last value in the bucket
  • 'max' — the maximum
  • 'min' — the minimum
  • 'sum' — the sum

• window Optional[int] = None Make this a bounded rolling-window frame showing only the last window rows (see Bounded rolling window). Requires exactly one of max_lookback / indicators.

• max_lookback Optional[int] = None With window, the largest indicator lookback you intend to use; the frame keeps window + max_lookback rows of margin so cached indicators stay correct across the automatic front-drop.

• indicators Optional[list[str]] = None With window, derive max_lookback from the largest lookback among these directives instead of stating it (e.g. ['atr:14', 'ma:50'] keeps a 50-row margin).

Bounded rolling window

Pass window= to cap the frame at the last window rows. This is the live-trading / NN-input shape: you keep append-ing bars forever, but memory stays bounded — the frame transparently drops old rows once it has accumulated enough, while retaining a hidden max_lookback-row margin so cached indicators remain bit-exact across each drop (the same values an unbounded frame would compute).

# A bounded 30-bar window; the margin is sized from the indicators you declare.
wf = DataFrame(seed, time_frame='15m', window=30, indicators=['atr:14'])

for bar in feed:            # runs forever; memory never grows
    wf.append(bar)          # fold a 1m bar into the forming 15m bar
    wf.fulfill()            # refresh the cached atr:14 tail (O(lookback))
    if wf.ready:            # warmed up: all 30 rows have valid history
        x = wf[['close', 'atr:14']].to_numpy('float32')   # the 30×2 feature window

Every row-facing surface — len, shape, index, indexing ([] / .iloc / .loc / .iat / .at), head / tail, reductions, to_numpy, to_csv, to_pandas, repr — shows only the window rows; the margin is never visible.

• ready bool Whether the window has warmed up (window + max_lookback rows accumulated, so every visible row has a full indicator history). Always True for an unbounded frame.
• fill_into(out, columns=None) Write the window's values into a caller-preallocated C-contiguous float32 / float64 array of shape (len(df), k), in place — the zero-allocation feature-export hot path (reuse one buffer across every bar). A missing cell becomes NaN; a string column is rejected (no float value).

DataFrame.from_pandas(pdf) -> DataFrame

A volas-specific static method that builds a DataFrame from a pandas.DataFrame (pdf) — the inverse of df.to_pandas(). pandas is imported lazily (only here, so volas stays pandas-free at import). A nullable column keeps its dtype + volas.NA, and a DatetimeIndex (tz-aware too) round-trips. See pandas interop.

df = DataFrame.from_pandas(pandas_df)   # pandas.DataFrame -> volas DataFrame

DataFrame.from_arrow(data) -> DataFrame

A volas-specific static method that builds a DataFrame from any object exposing the Arrow C-Stream protocol (__arrow_c_stream__) — a pyarrow.Table / RecordBatch / RecordBatchReader, a polars DataFrame, etc. The data buffers are borrowed where the dtypes match (otherwise a column is copied), a multi-chunk source is concatenated, and the result carries a fresh RangeIndex.

• data the Arrow source — any object implementing __arrow_c_stream__.

df = DataFrame.from_arrow(pa_table)        # pyarrow.Table     -> DataFrame
df = DataFrame.from_arrow(polars_df)       # polars.DataFrame  -> DataFrame

Arrow is not accepted by the DataFrame(data=...) constructor (which takes a dict or another DataFrame); build from an Arrow object through from_arrow.

df.exec(directive: str) -> np.ndarray

Evaluates the given directive and returns its values as a numpy ndarray. It is a pure, stateless evaluation — the frame is never modified (no column is created, and no cache is read or written).

# Compute the directive without touching the frame
df.exec('ma:20')

The difference between df[directive] and df.exec(directive) is that

• df[directive] creates a column for the result and caches it (incrementally refreshed after an append), while df.exec(directive) computes fresh every time and leaves the frame untouched — for a cached ndarray, use df[directive].to_numpy()
• df[directive] also accepts other indexing targets (a column name, a list, a boolean mask, a slice), while df.exec(directive) only accepts a valid volas directive string
• df[directive] returns a Series or DataFrame object while df.exec(directive) returns an np.ndarray

df.get_column(key: str) -> Series

Directly gets the column value by key, returning a Series — and never computes: unlike df[key], which parses an unknown key as an indicator directive and executes it, get_column only fetches an existing column and raises KeyError otherwise. Use it whenever the column name comes from external data (CSV headers, user input, configuration), so a name that happens to look like a directive (e.g. "ma:5") can never silently trigger a computation.

If the given key is an alias name, it returns the value of the corresponding original column. If the column is not found, a KeyError is raised.

df = DataFrame({
    'open' : ...,
    'high' : ...,
    'low'  : ...,
    'close': [5, 6, 7, 8, 9]
})

df.get_column('close')
# 0    5
# 1    6
# 2    7
# 3    8
# 4    9
# Name: close, dtype: int64

df.append(other: DataFrame | Row | dict) -> DataFrame

Appends rows of other to the end of the caller in place, returns the same DataFrame, and applies the DatetimeIndex to the newly-appended row(s) if possible. Use copy() first when the original frame must stay unchanged.

other is a DataFrame, a Row, or a scalar bar dict — one bar written as {column: value} with the bar's timestamp under the key equal to the index's name (a RangeIndex auto-increments). The dict form is the fast live path — it builds the bar straight into the frame with no per-bar 1-row DataFrame:

df.append({'time_key': ts, 'open': o, 'high': h, 'low': l, 'close': c, 'volume': v})

It is strict: every data column must be provided (a missing one raises — unlike a DataFrame / Row, where a missing column is NA-padded), and an unknown key raises. Cached directive columns are not supplied — they are padded and refreshed automatically.

If the caller is a tf-aware DataFrame (one built with a time_frame, or the result of cumulate), append instead folds each finer bar into the forming bar rather than adding a row — see Live cumulation.

append is lazy: it does not recompute the indicator columns of the new rows. They stay stale until an indicator-column read refreshes them or df.fulfill() is called (see below).

df.cumulate(time_frame: TimeFrame | str, cumulators: dict | None = None) -> DataFrame

Cumulate (resample) the data frame to a coarser time_frame, returning a new DataFrame. Requires a DatetimeIndex.

• time_frame TimeFrame | str the target bar interval, e.g. TimeFrame.m5 or '5m'. See TimeFrame.
• cumulators? dict[str, str] | None = None per-column aggregator overrides (e.g. {'amount': 'sum'}). Defaults to OHLCV semantics (open=first, high=max, low=min, close=last, volume=sum; any other column last). Each dict value is one of:
  • 'first' — the first value in the bucket
  • 'last' — the last value in the bucket
  • 'max' — the maximum
  • 'min' — the minimum
  • 'sum' — the sum

# from 1-minute klines to 5-minute klines
five_minute = one_minute.cumulate('5m')
fifteen_minute = one_minute.cumulate('15m')

five_minute.append(new_candle_1m)
# appending a 1-minute candle to a 5-minute DataFrame folds it into the 5m bar

fifteen_minute.append(new_candle_1m)
# so 1-minute data conveniently generates 5m and 15m test datasets

See Cumulation and DatetimeIndex for details.

df.fulfill() -> None

Batch-refresh every cached indicator column's stale tail in place (O(lookback + new rows) each, not an O(n) recompute), and return None.

Since append is lazy, the cache becomes fresh in one of two ways:

• Reading an indicator column — df['ma:20'] or df[['ma:20', 'rsi:14']] — auto-refreshes just those columns' stale tails on access, so a column read is always fresh and cheap. The single- and multi-column forms behave identically.
• Every other read — to_numpy(), .iloc / .loc / .at, the reductions (sum / mean / max / describe / …), to_csv, repr, … — does not auto-refresh; while the frame is stale it raises, telling you to call fulfill() first. This is deliberate: a half-updated frame fails loud instead of silently returning stale values, and you control when the (bounded) refresh cost is paid — which matters on a latency-sensitive live path.

df['ma:20']              # cache + read the 20-period SMA (fresh)
df.append(new_bar)       # lazy: the new row's ma:20 is now stale
df['ma:20']              # a column read auto-refreshes only the tail (fresh again)

df.append(new_bar)       # stale again
df.fulfill()             # batch-refresh every cached column's tail
df.to_numpy()            # now fresh (a bulk read would have raised while stale)

df.alias(as_name: str, src_name: str) -> None

Defines a column alias.

• as_name str the alias name
• src_name str the name of an existing column

# Some plot libraries such as `mplfinance` require a column named capitalized
# `Open`, but it is ok, we could create an alias.
df.alias('Open', 'open')

The alias resolves everywhere a column is looked up, including inside directives, and survives drop / copy / slicing.

df['Open']        # same data as df['open']
df['ma:5@Open']   # the alias resolves inside directives too

df.to_numpy(dtype=None, na_value=...) -> np.ndarray

The frame as a 2-D NumPy array (rows × columns). It tracks pandas except for one deliberate guard: an integer dtype over a frame that holds missing values raises instead of silently writing garbage (NumPy cannot store NA in an integer array) — give na_value to fill instead.

• dtype str | None — an optional export cast. None (the default) gives the honest per-dtype representation; otherwise:
  • 'object' (or 'O') — a lossless 2-D array of typed cells (number / str / Timestamp / volas.NA); the only dtype that keeps a str or datetime column intact.
  • 'int64', 'int32', 'int16', 'int8' (and the unsigned 'uint*') — the exact i64 channel (a large int and a datetime's epoch-ns survive without a float round trip). Over a frame with missing values this raises unless na_value is given.
  • 'float64', 'float32', 'float16' — the (lossy) float channel: a missing cell is NaN, a datetime past 2⁵³ ns quantises.
  • 'bool' — boolean.
  • 'datetime64[ns]' — datetime nanoseconds; a NaT cell is preserved.
  • A str column rejects every numeric / temporal dtype — use 'object'.
• na_value Any — the value substituted for each missing cell. Default: the NA-model representation (NaN / NaT / volas.NA).

df = DataFrame({'a': [1, 2, 3], 'b': [1.5, 2.5, 3.5]})

df.to_numpy()                 # -> float64 2-D array
df.to_numpy(dtype='int64')    # exact int64 cast (dense frame)
df.to_numpy(dtype='object')   # typed cells — lossless (numbers / str / Timestamp / volas.NA)

# an integer dtype over a missing value raises — unless na_value fills it
DataFrame({'a': [1, None]}).to_numpy(dtype='int64')               # ValueError
DataFrame({'a': [1, None]}).to_numpy(dtype='int64', na_value=0)   # -> [[1], [0]]  (int64)

Notes:

• The default (no dtype) is the honest representation: an all-numeric/bool frame is a float64 matrix (a missing cell → NaN), a frame containing str or mixed dtypes is an object matrix of typed cells, and a datetime frame is datetime64[ns].
• dtype='object' is always lossless — each cell keeps its own typed value (a number, a str, a Timestamp, or volas.NA).
• A str column has no numeric meaning, so any numeric/temporal dtype raises — use dtype='object' to keep the strings.
• A datetime column is exempt from the integer-NA raise: under dtype='int64' a NaT exports as its exact epoch-ns sentinel (datetime never round-trips through float); na_value overrides that sentinel when given.
• to_numpy() is a bulk read; it does not auto-refresh stale indicator columns and raises if any are stale — call df.fulfill() first.

For a zero-copy hand-off to Arrow / DLPack consumers, see Arrow & DLPack interop.

df.to_arrow() -> pyarrow.Table

A volas-specific export to a pyarrow.Table, zero-copy where the dtypes match — the numeric / string / datetime column buffers are shared with Arrow, while bool and the null bitmap are repacked. Requires pyarrow (imported lazily, only here). It is a convenience over volas's Arrow C-Stream bridge: any Arrow consumer can read the frame directly through the standard __arrow_c_stream__ PyCapsule protocol, with no to_arrow() call and without volas depending on pyarrow.

import pyarrow as pa
tbl = df.to_arrow()        # -> pyarrow.Table (shares the column buffers)
tbl = pa.table(df)         # identical, via the __arrow_c_stream__ protocol
pdf = pl.from_dataframe(df)  # polars reads it through the same protocol

Returns a pyarrow.Table. See Arrow & DLPack interop for the full zero-copy contract and the DLPack export.

df.to_pandas(dtype_backend='numpy') -> pandas.DataFrame

Export to a pandas.DataFrame (pandas is imported lazily, only here — it is not a runtime dependency). A DatetimeIndex round-trips, and the reverse bridge is DataFrame.from_pandas.

• dtype_backend? str = 'numpy' how a missing value is carried into pandas:
  • 'numpy' — the most ecosystem-compatible form: an int / bool column with a missing value becomes float64 / object with NaN (like pandas.Int64.to_numpy()).
  • 'numpy_nullable' — a faithful, lossless masked round-trip: an int / bool / str column stays Int64 / boolean / string with the hole as pandas.NA.

pdf = df.to_pandas()                                # 'numpy' backend (NaN-based)
pdf = df.to_pandas(dtype_backend='numpy_nullable')  # lossless masked Int64 / boolean / string

See pandas interop for the round-trip details and to_csv.

Series

df[col] and df[directive] return a Series — a named 1-D column whose API is pandas-compatible: arithmetic / comparison / logical operators, .sum() / .mean() / .std() / …, .shift() / .diff() / .fillna(), .iloc / .loc, .to_numpy() / .to_list(). See the rest of the pandas-compatible API for the full list. There is no public Series constructor — a Series is always obtained by indexing a DataFrame.

s = df['close']
s.name                 # 'close'
(s - s.shift(1)).mean()
df['ma:5 > ma:20']     # a directive likewise returns a Series (here a bool one)

Beyond pandas, a Series also exposes the 15 TA-Lib Math Transform functions as methods — acos asin atan ceil cos cosh exp floor ln log10 sin sinh sqrt tan tanh:

df['close'].ln()
df['high'].sqrt()

A datetime64[ns] Series exposes the pandas .dt accessor: calendar components (year month day hour minute second microsecond nanosecond quarter dayofweek dayofyear days_in_month), calendar predicates (is_month_start … is_year_end, is_leap_year), names (day_name() / month_name()), formatting (strftime(fmt)), bar alignment (floor(freq) / ceil(freq) / round(freq) / normalize()), and isocalendar(). A missing element yields NA in every component:

t = volas.to_datetime(df['time'])
t.dt.hour                  # int64 Series, 0..23
t.dt.dayofweek             # Monday=0 .. Sunday=6
t.dt.floor('15min')        # datetime Series aligned to the 15-minute bar

Series.from_arrow(data, name=None) -> Series

A volas-specific static method that builds a Series from any object exposing the Arrow C-Data array protocol (__arrow_c_array__) — a pyarrow.Array, a polars Series, etc. The data buffer is borrowed where the dtype matches (otherwise copied); the result carries a fresh RangeIndex.

• data the Arrow source — any object implementing __arrow_c_array__.
• name? str | None = None the name for the resulting Series.

s = Series.from_arrow(pa_array, name='close')   # pyarrow.Array -> Series

series.to_numpy(dtype=None, na_value=...) -> np.ndarray

The column values as a 1-D NumPy array — pandas.Series.to_numpy semantics:

• dtype str | None — an optional export cast. None (the default) gives the column's native representation; otherwise any NumPy dtype string accepted by numpy.ndarray.astype, the common values being:
  • 'int64', 'int32', 'int16', 'int8' (and the unsigned 'uint64', 'uint32', 'uint16', 'uint8') — integer. Over a column with missing values this raises unless na_value is given (an NA has no integer representation).
  • 'float64', 'float32', 'float16' — floating point; a missing cell is NaN.
  • 'bool' — boolean.
  • 'datetime64[ns]' — datetime nanoseconds; a missing cell is NaT.
  • 'object' (or 'O') — Python objects, each cell its own typed value (lossless).
• na_value Any — the value to substitute for each missing cell. Default: the NA-model representation (NaN for the float export, None in an object array). With an explicit integer dtype, the values stay exact (a large int is not funnelled through float64) and the holes become na_value.

series = DataFrame({'qty': [1, None, 3]})['qty']    # int64 with a missing value

series.to_numpy()                          # -> array([ 1., nan,  3.])   (float64; a missing int -> NaN)
series.to_numpy(dtype='int64', na_value=0) # -> array([1, 0, 3])         (int64; NA filled, dtype kept)
series.to_numpy(na_value=-1)               # -> array([ 1., -1.,  3.])   (default float export, NA -> -1)

# without na_value, an integer dtype over a missing value raises
series.to_numpy(dtype='int64')
# ValueError: cannot convert a column with missing values to integer NumPy dtype 'int64' ...

Notes:

• The default (no dtype, no na_value) is the dtype-specific export: a missing int / bool / datetime cell collapses to NaN / NaT (NumPy has no NA), while a dense column keeps its native dtype. A float NaN is in-band, so a float column cast to an integer dtype likewise raises when any value is NaN (pass na_value).
• Like pandas, na_value only changes the missing cells — without an explicit dtype an int column with NA still exports float64 (the default), and na_value simply fills the NaN slots.
• For a lossless NA round-trip that keeps the native dtype and the missing positions (no fill, no float collapse), use the Arrow path (series.to_arrow() carries the null bitmap) or series.to_pandas(dtype_backend='numpy_nullable'); the NA mask alone is series.isna().to_numpy().

series.to_arrow() -> pyarrow.Array

A volas-specific export of the column to a pyarrow.Array, zero-copy where the dtype matches (the numeric / string / datetime buffer is shared; bool and the null bitmap are repacked). Requires pyarrow (imported lazily). It is a convenience over volas's Arrow C-Data bridge: any Arrow consumer can read the series directly through the standard __arrow_c_array__ PyCapsule protocol.

import pyarrow as pa
arr = series.to_arrow()    # -> pyarrow.Array (shares the buffer)
arr = pa.array(series)     # identical, via the __arrow_c_array__ protocol

Returns a pyarrow.Array. The column also exports zero-copy to NumPy / PyTorch / JAX via DLPack (np.from_dlpack(series)) — see Arrow & DLPack interop.

Row

df.iloc[i] and df.loc[label] return a Row — a single record whose .name is its index label. A Row has no public constructor (Row(...) raises TypeError: No constructor defined for Row); you only obtain one by indexing a frame, and you may pass it to df.append.

row = df.iloc[-1]      # the latest bar
row.name               # its index label (e.g. a Timestamp for a DatetimeIndex)
row.to_dict()          # {column: value}
row.to_numpy()         # the numeric cells as a 1-D ndarray

Live cumulation — a tf-aware DataFrame

For live streaming, give a DataFrame a time_frame and append finer bars into it, instead of re-cumulating the whole frame each tick. df.cumulate(tf) returns such a frame (the forming period kept live), or build one directly with DataFrame(data, time_frame=..., cumulators=...) (the given rows are taken as already-final bars at that frame; requires a DatetimeIndex).

On a tf-aware frame:

• df.append(bar) folds the bar in: one in the open period updates the forming last row (df.iloc[-1]); one in a new period rolls over into a fresh row; a re-sent forming bar (same timestamp) updates rather than double-counts.
• df.iloc[-1] is the current (still-open) period — the live bar.
• df[directive] / df.exec(directive) computes indicators over the cumulated frame including the forming row — lazily, on read: an append only marks them stale, and the next read recomputes just the tail.
• df.cumulate(target) must be a whole multiple of the source frame (e.g. 5m→15m, not 5m→7m; a week or 3-day bar does not nest into a month/year); the same frame is a copy().

df = history.cumulate('5m')   # a tf-aware 5m frame (history is finer, e.g. 1m)
for bar in stream:            # each `bar` is a finer DataFrame
    df.append(bar)            # folds into the forming 5m bar
    df.iloc[-1]               # the live, still-forming bar
    df['macd']               # indicators over the cumulated frame

See Cumulation and DatetimeIndex for details.

read_csv(path, sep=',', header=True, parse_dates=None, index_col=None, na_values=None, keep_default_na=True, tz=None, date_unit=None) -> DataFrame

A top-level function that reads a CSV file into a DataFrame, inferring per-column dtypes — a fast, pandas-subset CSV reader.

• path str | os.PathLike the CSV file path — a string or any os.PathLike (e.g. pathlib.Path).
• sep? str = ',' the field delimiter (a single character); delimiter is an accepted alias.
• header? bool = True True (or omitted) treats the first row as the header; False / None means no header (columns are named '0'…'n-1').
• parse_dates? list[str] | None = None column names to parse into datetime columns.
• index_col? str | int | None = None a column name or integer position to move into the row index; applied after parse_dates, so naming a parsed date column yields a DatetimeIndex.
• na_values? str | list[str] | None = None extra missing-value tokens.
• keep_default_na? bool = True also treat the default NA tokens as missing.
• tz? str | None = None the timezone for the index_col datetime: a naive date string is read in tz (stored UTC, the index tagged). Pass the date column via index_col and do not also list it in parse_dates. See Timezones. Accepts either:
  • a fixed UTC offset, e.g. '+08:00' / '-05:00'
  • an IANA timezone name, e.g. 'America/New_York' / 'Asia/Shanghai' / 'UTC'
• date_unit? str | None = None read index_col as an epoch integer in this unit (absolute UTC; tz then only sets the display zone). One of:
  • 's' — seconds
  • 'ms' — milliseconds
  • 'us' — microseconds
  • 'ns' — nanoseconds

from volas import read_csv

df = read_csv('klines.csv')                        # RangeIndex
df = read_csv('klines.csv',
              parse_dates=['time_key'],            # parse to datetime
              index_col='time_key')                # -> DatetimeIndex
df = read_csv('data.tsv', sep='\t', header=False,  # no header -> '0'..'n-1'
              na_values=['NA', 'null'])

to_datetime(obj, unit='ns', format=None) -> Series

A top-level function that converts epoch numbers or datetime strings to a datetime Series, mirroring pandas.to_datetime. obj may be a Series, a 1-D NumPy array, or a list. A missing input (a float NaN, or a volas.NA in an int column) becomes NaT, like pd.to_datetime.

• obj the values to convert — numeric epochs, datetime strings, or an already-datetime Series (returned unchanged).
• unit? str = 'ns' the epoch unit for numeric input (sub-unit fractions are preserved, like pd.to_datetime). One of:
  • 's' — seconds
  • 'ms' — milliseconds
  • 'us' — microseconds
  • 'ns' — nanoseconds (the default)
• format? str | None = None an explicit datetime format for string input (pandas format=, e.g. '%Y-%m-%d %H:%M:%S') — faster and unambiguous; ignored for numeric input. Any strftime/strptime directive string; None auto-infers.

Naive strings parse as UTC and offset-aware strings (…+08:00) are absolute. To display the resulting index in a zone, make it the index and tag the zone with tz_localize / tz_convert (see Timezones).

from volas import to_datetime

# parse an epoch-seconds column to datetime, then make it the index
df['time'] = to_datetime(df['time'], unit='s')
df = df.set_index('time')                       # -> DatetimeIndex
df = df.tz_localize('America/New_York')         # tag the display zone (see Timezones)

For an in-place, truncating cast (the NumPy / pandas astype idiom), use df.astype({'time': 'datetime64[s]'}) instead.

directive_stringify(directive: str) -> str

Get the canonical full name of a directive — the actual column name volas caches it under. The command name is lowercased and default arguments / series are dropped to save space.

from volas import directive_stringify

directive_stringify('kdj.j')
# 'kdj.j'

directive_stringify('kdj.j:9,3,2,100@high,close,close')
# 'kdj.j:,,2,100@,close'

# command names are case-insensitive and canonicalize to lowercase
directive_stringify('MACD:12,26')
# 'macd'

directive_lookback(directive: str) -> int

Get the lookback period of a directive — the minimum number of prior data points required before the indicator produces a valid result.

from volas import directive_lookback

directive_lookback('ma:20')
# 19

directive_lookback('boll')
# 19 (default period 20)

# Compound directive: lookback accumulates across nested expressions.
# repeat:5 needs 4 extra points, boll.upper (period 20) needs 19 -> 23
directive_lookback('repeat:5@(close > boll.upper)')
# 23

The rest of the pandas-compatible API

Everything below behaves like its pandas counterpart — if you know it from pandas, it works the same in volas, except for the deliberate NA-model divergences noted after the listing.

# --- DataFrame: metadata --------------------------------------------------
df.columns / df.shape / len(df) / df.dtypes      # dtypes -> dict
df.index                          # row labels, as a NumPy array
col in df ; for col in df         # membership / iterate column names
df.tz / df.tz_localize(tz) / df.tz_convert(tz)   # DatetimeIndex tz; see Timezones

# --- DataFrame: selection -------------------------------------------------
df[col]                           # -> Series
df[[col, ...]]                    # -> DataFrame
df[bool_mask]                     # -> DataFrame (filter rows; mask = Series | ndarray)
df.iloc[...] / df.loc[...] / df.at[label, col] / df.iat[i, j]
df.head(n=5) / df.tail(n=5)

# --- DataFrame: reshaping & dtypes ----------------------------------------
df.drop([label, ...], axis=0)     # drop rows by label (axis=1 -> columns)
df.dropna(how='any') / df.sort_index(ascending=True) / df.reset_index(drop=False)
df.rename({old: new}) / df.astype({col: dtype}) / df.set_index(col)
df.astype({col: 'datetime64[s]'})  # numeric epoch -> datetime (unit s|ms|us|ns; truncating)
df.copy() / df.equals(other) / df.to_csv(path=None, ...)   # to_numpy: see its own section (dtype, na_value)

# --- DataFrame: writing ---------------------------------------------------
df[col] = scalar | array | Series          # add / replace a column (positional)
df.loc[mask, col] = value ; df.iloc[i, j] = value ; df.at[label, col] = value

# --- Series ---------------------------------------------------------------
s.name / s.dtype / len(s) / s.tz / s.index
s.to_list()                       # to_numpy has NA caveats -> see its own section (dtype, na_value)
s.iloc[...] / s.loc[...]
s + s, s - 1, -s, ...             # elementwise arithmetic
s > 0, s == t, s != t, ...        # comparison -> bool Series
s & t, s | t, ~s, s ^ t           # logical -> bool Series
s.sum() / s.mean() / s.min() / s.max() / s.std() / s.var() / s.median()   # skip missing
s.shift(n=1) / s.diff(n=1) / s.fillna(v) / s.ffill() / s.bfill()           # see Missing values: NA keeps the dtype
s.isna() / s.notna() / s.dropna() / s.equals(t)

Window operations (rolling / expanding / ewm) — compatibility only

This surface exists so pandas research / labeling code moves over verbatim. It is NOT the recommended way to compute indicators, and it should NOT be used in a live trading system: a window result is a plain Series — it does not join the directive cache and is not incrementally refreshed by append() / fulfill(); every new bar costs a full O(n) recompute. Prefer the equivalent directive (df['ma:20'], df['median:30'], df['stddev:20'], …): same kernels, plus caching and O(lookback) per-bar refresh.

s.rolling(window, min_periods=None, center=False)   # int window; min_periods defaults to window
s.expanding(min_periods=1)
s.ewm(com=|span=|halflife=|alpha=, min_periods=0, adjust=True, ignore_na=False)
                                                    # exactly ONE decay spelling

# Rolling / Expanding (pandas semantics: NA skipped, min_periods gates):
.count() .nunique()                                 # -> int64 Series (native NA)
.sum() .mean() .median() .min() .max()
.var(ddof=1) .std(ddof=1) .sem(ddof=1) .skew() .kurt()
.quantile(q, interpolation='linear') .rank(method='average', ascending=True, pct=False)
.first() .last()                                    # dtype-preserving
.corr(other) .cov(other, ddof=1)

# Ewm:
.mean() .sum() .var(bias=False) .std(bias=False) .corr(other) .cov(other, bias=False)

center=True labels each window at its center — it reads future bars relative to the label. That is exactly what a labeling pass wants, and exactly what a live signal must never do; it is supported for the former.

Time-based windows (rolling('5min') / a timedelta) are deliberately not implemented. For multi-timeframe computation, maintain two tf-aware DataFrames (see Cumulation) and append each bar to both — that is the supported, O(lookback)-per-bar design; emulating a coarser timeframe through window arithmetic recomputes everything on every bar.

Not provided (pandas members that conflict with volas's model): apply / agg / pipe (arbitrary-Python-per-window), win_type, step, on, closed, method, ewm(times=...), ewm.online() — append() + directives already cover the streaming use case.

Known pandas divergences (the volas.NA model)

A handful of APIs diverge from pandas by design, because volas stores missing values natively as volas.NA (no object dtype, no silent float upcast):

• shift / diff / fillna and friends keep the column's dtype — a missing value is volas.NA, not an int/bool/str column upcast to float/object.
• Comparisons (== != < <= > >=) return a non-nullable bool mask: a missing value compares False (and != compares True), following IEEE / NumPy — not pandas-nullable's three-valued NA. This keeps masks free of NA so df[mask] and assignment stay total.
• Storage keeps the dtype. Where pandas upcasts an int/bool column with a missing value to float64 / object, volas keeps it int64 / boolean with the hole as volas.NA — so to_list() returns exact ints and volas.NA. The numpy export (to_numpy()) still follows pandas 3.0 exactly: a missing cell becomes NaN / NaT by default, an integer dtype= over missing values raises, and na_value= fills — see the dedicated df.to_numpy / series.to_numpy sections above.

For the full picture — why volas's type system is built this way, where pandas's breaks, and the migration gotchas — see volas vs pandas — the type system.

The pandas-shaped indexing and writing details have their own sections — Indexing & selection and Writing & assignment.

Cumulation and DatetimeIndex

Suppose we have a csv file containing kline data of a stock in the 1-minute time frame:

csv = read_csv(csv_path)

print(csv)

                   date   open   high    low  close    volume
0   2020-01-01 00:00:00  329.4  331.6  327.6  328.8  14202519
1   2020-01-01 00:01:00  330.0  332.0  328.0  331.0  13953191
2   2020-01-01 00:02:00  332.8  332.8  328.4  331.0  10339120
3   2020-01-01 00:03:00  332.0  334.2  330.2  331.0   9904468
4   2020-01-01 00:04:00  329.6  330.2  324.9  324.9  13947162
5   2020-01-01 00:04:00  329.6  330.2  324.8  324.8  13947163    <- an update of
                                                                    2020-01-01 00:04:00
...
19  2020-01-01 00:19:00  327.0  327.2  322.0  323.0  15086985

Note that duplicated records of the same timestamp are not cumulated. All records except the latest one are discarded.

Read the same csv, but parse the date column into a DatetimeIndex:

df = read_csv(
    csv_path,
    parse_dates=['date'],
    index_col='date'
)

print(df)

                      open   high    low  close    volume
2020-01-01 00:00:00  329.4  331.6  327.6  328.8  14202519
2020-01-01 00:01:00  330.0  332.0  328.0  331.0  13953191
...
2020-01-01 00:19:00  327.0  327.2  322.0  323.0  15086985

You must have figured it out that the data frame now has a DatetimeIndex.

But it will not become a 5-minute kline unless we cumulate it:

df_5m = df.cumulate('5m')

print(df_5m)

Now we get a 5-minute kline:

                      open   high    low  close      volume
2020-01-01 00:00:00  329.4  334.2  324.8  324.8  62346461.0
2020-01-01 00:05:00  325.0  327.8  316.2  322.0  82176419.0
2020-01-01 00:10:00  323.0  327.8  314.6  327.6  74409815.0
2020-01-01 00:15:00  330.0  335.2  322.0  323.0  82452902.0

cumulate defaults to OHLCV semantics — open=first, high=max, low=min, close=last, volume=sum — and any other column falls back to last. Pass cumulators= to override a column's aggregator; the common case is a non-OHLCV column that should be summed, such as a turnover (amount) column that would otherwise default to last:

df.cumulate('1h', cumulators={'amount': 'sum'})

The supported aggregators are first, max, min, last and sum.

The time_frame may be a string label or a TimeFrame constant — see TimeFrame for the full list.

Bar labels are the period start

Every time frame lies on a fixed grid, and a cumulated bar is labelled with its period's grid start — even when the first raw bar arrives mid-period. A bar that opens with a 09:07 tick on a 15m frame is labelled 09:00, never 09:07, so volas bars line up exactly with exchange klines and with pandas resample (label='left').

The grid origins per frame: intraday frames anchor at midnight of the index's (timezone-aware) trading day — a 15m bar starts at :00/:15/:30/ :45, a 4h bar at 00:00/04:00/…; 1d starts at midnight; 1w on Monday; 3d is a continuous grid from the Unix epoch; 1M / 1y on the calendar month / year. If a daylight-saving transition removes or repeats a period's boundary, the label resolves to the period's earliest real instant.

For live streaming you do not re-cumulate the whole history on every tick — you keep the current 5-minute bar forming and update it as each finer bar arrives. A tf-aware DataFrame does exactly that: it stays an ordinary DataFrame (read columns, run directives, slice it), except append folds each finer bar into the bar currently forming instead of adding a row. You make one with df.cumulate('5m') or DataFrame(data, time_frame='5m'), and the live loop is then just:

step	call
make a 5m frame	cum = df.cumulate('5m')
feed it the next finer bar	cum.append(bar)
read the current forming bar	cum.iloc[-1]
read an indicator over it	cum['macd']

Watch the forming bar grow

Build the 5-minute frame from the 1-minute df above one bar at a time. Seed it with the 00:00 bar, then fold in 00:01. Both fall in the same 00:00–00:05 window, so the frame still holds one row — the forming bar — now updated (high rose to 332.0, close to 331.0, volume summed):

cum = df.iloc[0:1].cumulate('5m')   # seed the 5m frame with the 00:00 bar
cum.append(df.iloc[1:2])            # fold in 00:01 (same 5m window)

print(cum)

                      open   high    low  close      volume
2020-01-01 00:00:00  329.4  332.0  327.6  331.0  28155710.0

Fold in 00:02, 00:03 and 00:04 and the window fills up. That single forming row is now the finished first 5-minute bar — identical to the first row of the one-shot df.cumulate('5m') printed earlier:

for i in range(2, 5):
    cum.append(df.iloc[i:i + 1])

print(cum)

                      open   high    low  close      volume
2020-01-01 00:00:00  329.4  334.2  324.8  324.8  62346461.0

Now fold in 00:05. It opens the next window, so the 00:00 bar is finalized and a fresh forming bar starts; the frame grows to two rows and cum.iloc[-1] is the new, still-forming 00:05 bar:

cum.append(df.iloc[5:6])

print(cum)

                      open   high    low  close      volume
2020-01-01 00:00:00  329.4  334.2  324.8  324.8  62346461.0   <- finalized
2020-01-01 00:05:00  325.0  327.8  324.8  327.6  10448427.0   <- still forming

Two properties make this safe for a live feed:

• Indicators are lazy, and fresh on read. append does not recompute anything — it only flags the dependent directive columns as stale (their valid-row cursor now lags the frame height). The recompute happens when you read cum['ema:9'] (or any directive): only the stale tail is refreshed — O(lookback), not the whole column — over the frame including the forming row, bit-identical to a one-shot cumulate-then-compute. (A bulk read such as to_numpy() does not auto-refresh; call cum.fulfill() first, or just read the directive.)
• Re-sent bars do not double-count. Folding a bar whose timestamp you have already seen updates that period instead of adding to it — the same dedup rule shown at the top of this section — matching exchanges that revise their most recent bar.

See Live cumulation for the API summary.

TimeFrame

A TimeFrame names a bar interval. It is accepted anywhere volas resamples — df.cumulate, the time_frame DataFrame argument, and the hv indicator — either as a TimeFrame constant or as its equivalent string label. There is no TimeFrame(...) constructor — use one of the constants below or a label string.

TimeFrame.m5            # the 5-minute frame
'5m'                    # the equivalent label string, accepted everywhere too

df.cumulate(TimeFrame.m5)     # identical to df.cumulate('5m')

Supported frames (constant ⇄ label):

Constant	Label	Alignment
TimeFrame.s1	'1s'	Civil second.
TimeFrame.m1	'1m'	Civil minute.
TimeFrame.m3	'3m'	Minute-of-hour buckets starting at 00, 03, 06, ...
TimeFrame.m5	'5m'	Minute-of-hour buckets starting at 00, 05, 10, ...
TimeFrame.m15	'15m'	Minute-of-hour buckets starting at 00, 15, 30, 45.
TimeFrame.m30	'30m'	Minute-of-hour buckets starting at 00 and 30.
TimeFrame.H1	'1h'	Civil hour.
TimeFrame.H2	'2h'	Hour-of-day buckets starting at 00, 02, 04, ...
TimeFrame.H4	'4h'	Hour-of-day buckets starting at 00, 04, 08, ...
TimeFrame.H6	'6h'	Hour-of-day buckets starting at 00, 06, 12, 18.
TimeFrame.H8	'8h'	Hour-of-day buckets starting at 00, 08, 16.
TimeFrame.H12	'12h'	Hour-of-day buckets starting at 00 and 12.
TimeFrame.D1	'1d'	Civil day in the frame timezone.
TimeFrame.D3	'3d'	Continuous 3-day buckets anchored to the Unix epoch; they do not reset at month boundaries.
TimeFrame.W1	'1w'	Continuous Monday-start weeks, including runs that cross month boundaries.
TimeFrame.M1	'1M'	Civil calendar month in the frame timezone.
TimeFrame.Y1	'1y'	Civil calendar year in the frame timezone.

Every bucket is aligned in the frame timezone's local wall-clock while storage stays UTC: the hour-of-day frames (2h/4h/6h/8h/12h) start at local 00 and step in local hours; 3d counts continuous 3-local-civil-day buckets keyed from the Unix epoch day in that zone (not reset at month boundaries); 1w is Monday-start in local civil time. So a daily/weekly bar follows the local trading day, and a named zone makes the hour buckets DST-aware.

Syntax of directive

command . sub : args @ series  op  command ...
   |      |     |      |
   |      |     |      └── operand column / sub-expression  (e.g. @open, @(boll))
   |      |     └── comma-separated arguments               (e.g. ma:20, kdj.k:9,3)
   |      └── sub-command                                   (e.g. macd.signal)
   └── indicator name                                       (e.g. ma, macd, boll)

directive Example

Here lists several use cases of column names

# The middle band of bollinger bands
#   which is actually a 20-period (default) moving average
df['boll']

# kdj j less than 0
# This returns a series of bool type
df['kdj.j < 0']

# kdj %K cross up kdj %D
df['kdj.k // kdj.d']

# 5-period simple moving average
df['ma:5']

# 10-period simple moving average on (@) open prices
df['ma:10@open']

# A DataFrame of 5-period, 10-period and 30-period ma
df[[
    'ma:5',
    'ma:10',
    'ma:30'
]]

# Which means we use the default values of the first and the second parameters,
# and specify the third parameter (for macd.signal)
df['macd.signal:,,10']

# We must wrap a parameter which is a nested command or directive
df['increase:3@(ma:20@close)']

# volas has a powerful directive parser,
# so we could even write directives like this:
df['''
repeat
    :   5
    @   (
            close > boll.upper
        )
''']

Operators

left operator right

• // — whether left crosses up through right (from below to above), which we call a "gold cross": df['macd // macd.signal'].
• \\ — whether left crosses down through right, a "dead cross". In a Python string the backslash must be escaped, so we write 'macd \\ macd.signal'.
• >< — whether left crosses right, either up or down.
• < <= == != >= > — for the same record, the value comparison between left and right, returning a bool series.
• arithmetic + - * /, logical & | ^, and unary ~ (not) / - (negate).

df[directive] caches the result as a real column (so repeated reads are free), then auto-refreshes its stale tail on access after an append. Use df.exec(directive) to compute a directive as a NumPy array without caching it (see Usage).

Indexing & selection

A pandas-compatible subset for label and positional access. The row index may be a range, a DatetimeIndex, an integer index, or a string index.

df.iloc[2]          # a Row by position (row.name is its index label)
df.iloc[10:]        # a DataFrame slice by position
df.loc[label]       # a Row by index label
df.loc[lo:hi]       # inclusive label slice (lexicographic for string indexes)
df.at[label, col]   # a scalar by label + column
df.iat[i, j]        # a scalar by position
df.index            # the row labels, as a NumPy array

String (symbol) index — set_index on a string column, then look up by symbol:

df = DataFrame({'sym': ['aa', 'bb', 'cc'], 'px': [1.0, 2.0, 3.0]}).set_index('sym')
df.loc['bb']           # the row keyed 'bb'
df.loc['aa':'bb']      # inclusive, lexicographic slice
df.at['cc', 'px']      # 3.0
df.drop(['bb'])        # drop by string label

Differences from pandas (vs pandas)

volas is pandas-shaped on the surface, but its type system is deliberately different in more than the index: missing values keep their dtype, there is no object dtype, value-returning methods stay Series, and a lossy conversion raises instead of degrading silently. See volas vs pandas — the type system for the full comparison — why volas is built this way, where pandas's type system breaks, and the migration gotchas.

The index specifically is a single level of one homogeneous label type. Relative to pandas, volas does not support:

• MultiIndex (hierarchical / multi-level indexes), on rows or columns — columns are a flat list of unique string names.
• Arbitrary label dtypes — an index is exactly one of range, datetime (datetime64[ns]), integer, or string. There is no float, categorical, interval, period, timedelta, or mixed-type object index.
• Index algebra — reindexing, index set operations (union / intersection), and automatic alignment-on-index when combining frames.
• Duplicate-label lookups (label access assumes unique labels).

If your workflow needs any of these, keep using pandas; volas targets the single-level, OHLCV-shaped index that candlestick data uses.

Writing & assignment

Assign a whole column, or write into a positional / label / boolean selection (copy-on-write under the hood). Series assignment is positional (by row order, not index-aligned).

df['signal'] = 0.0                      # add / replace a column (scalar | array | Series)
df.iat[3, 0] = 99.0                     # one cell by position
df.at[label, 'close'] = 99.0            # one cell by label + column
df.iloc[10:20, 0] = 0.0                 # a column slice
df.loc[df['close'] > df['open'], 'signal'] = 1.0   # masked column assignment

Writing a fractional value into an integer column raises — the int dtype is kept, and a lossy write errors rather than silently widening to float (see Differences from pandas; writing volas.NA / None keeps the int dtype and marks the cell missing). Writing into a cached directive column drops its cached status, so a later fulfill() can never silently overwrite your edit.

Timezones

Storage is always UTC epoch-nanoseconds — the universal axis on which crypto, US, HK and A-share frames coexist and align on the absolute instant. A DatetimeIndex additionally carries a per-frame timezone that governs how those instants render, how bare-string labels match, and how cumulate aligns day-and-coarser buckets. A timezone is either a fixed offset ('+08:00', cheap; crypto / A-share / HK) or a named IANA zone ('America/New_York', DST-aware via chrono-tz; US / EU). The default is UTC.

Here is the whole picture. Build a DatetimeIndex by parsing a column with to_datetime, promoting it with set_index, then tagging the display zone with tz_localize (reinterpret a naive wall-clock as that zone — the instant moves) or tz_convert (keep the instant, restate the zone). A US exchange opens at 09:30 local on 2021-01-04, held as a naive local string:

from volas import DataFrame, to_datetime, Timestamp

# Parse the naive 't' strings to UTC instants and make them the index, then read
# the wall-clock *as New York local time* with tz_localize. The instant is stored
# UTC (14:30Z), but the index renders and matches in New York.
df = DataFrame({'t': ['2021-01-04 09:30:00'], 'close': [100.0]})
df['t'] = to_datetime(df['t'])
df = df.set_index('t').tz_localize('America/New_York')
df.tz       # 'America/New_York'
df.index    # ['2021-01-04T14:30:00.000000000']  (raw .index is UTC, matching pandas .values)

# The tz is what lets a bare local string match the right row — it is parsed in df.tz:
df.at['2021-01-04 09:30:00', 'close']   # 100.0

# A Timestamp is a typed, cross-tz label. The SAME instant in Shanghai is
# 22:30+08:00, and it still matches, regardless of df.tz:
ts = Timestamp('2021-01-04 22:30:00', tz='+08:00')   # == 09:30 New York
df.at[ts, 'close']                       # 100.0
ts.value                                 # its UTC epoch-nanoseconds (int)
ts.tz                                    # '+08:00'

# Integer epochs: to_datetime(unit=...) reads the unit. An epoch is *absolute* —
# anchor it as UTC, then restate the zone for display. 1609770600000 ms == 14:30Z:
e = DataFrame({'t': [1609770600000], 'close': [100.0]})
e['t'] = to_datetime(e['t'], unit='ms')
e.set_index('t').tz_localize('UTC').tz_convert('America/New_York').index
# ['2021-01-04T14:30:00.000000000']

# An offset-aware string is already absolute too — to_datetime resolves the offset:
o = DataFrame({'t': ['2021-01-04T09:30:00+08:00'], 'close': [1.0]})
o['t'] = to_datetime(o['t'])
o.set_index('t').index
# ['2021-01-04T01:30:00.000000000']  (09:30+08:00 == 01:30Z)

A frame's time axis is in one of two states (the pandas model): naive (an unanchored wall-clock, df.tz is None) or tz-aware (anchored, df.tz names the zone — 'UTC' included). tz_localize anchors a naive axis (the instant moves to match the wall-clock in that zone); tz_convert restates an aware axis in another zone (the instant is unchanged). Each refuses the other state — converting an unanchored clock or re-anchoring an anchored one would silently shift instants:

naive = df                                   # df.tz is None
aware = naive.tz_localize('America/New_York')   # anchor: instants move, wall-clock kept
aware.tz_convert('+08:00')                   # restate: instants kept, wall-clock moves
naive.tz_convert('+08:00')                   # TypeError — anchor with tz_localize first
aware.tz_localize('UTC')                     # TypeError — already anchored; use tz_convert

cumulate to a daily (or coarser) bar aligns buckets to the frame's local trading day — DST-aware for a named zone — while the raw .index numpy export stays UTC (matching pandas .values).

Missing values (volas.NA)

volas.NA is the single missing-value marker, and every dtype supports it — crucially, a missing value never changes the column's dtype:

dtype	how missing is stored	element access	console display
float64 / float32	NaN, in-band	np.float64(nan)	<NA>
int64 / int32 / bool / str	a validity mask (dtype kept)	volas.NA	<NA>
datetime64[ns]	NaT	np.datetime64('NaT')	<NA>

Whatever the storage, the console always prints <NA> — one symbol for a missing value, regardless of dtype (a float NaN, a datetime NaT, and an int / bool / str hole all render identically; to_string(na_rep=...) overrides it). Element access and to_numpy stay dtype-specific (a float hole reads back as np.nan), so numpy / pandas interop is lossless.

This tracks pandas' own direction (PDEP-16) and means volas has no object dtype: an int / bool / str column with a hole stays int / bool / str, where pandas 3.0 upcasts to float64 / object.

import volas
s = volas.DataFrame({'a': [1, None, 3]})['a']
s.dtype                  # 'int64'        (pandas would give float64)
s[1]                     # <NA>           (s[1] is volas.NA; a float hole stays np.nan)
s.sum()                  # np.int64(4)    reductions skip NA
s.fillna(0).to_list()    # [1, 0, 3]
s.isna().to_numpy()      # [False, True, False]
print(s)                 # the missing cell prints as <NA>

# shift / diff keep the int dtype (pandas upcasts to float); the gap is NA:
volas.DataFrame({'a': [10, 20, 30]})['a'].shift(1).to_list()   # [<NA>, 10, 20]

• Producing NA — None (or volas.NA) in a constructor list, the shift / diff gap, and the default fill of where / mask.
• Consuming NA — reductions (sum / mean / min / …) and count skip it; arithmetic propagates it (x ∘ NA = NA); ~ / & / | / ^ use Kleene three-valued logic (NA & False = False, NA | True = True); cumsum / abs / round / clip / indexing carry it through; isna / notna / dropna / fillna / ffill / bfill work on every dtype.
• Comparisons treat a missing value IEEE / numpy style: ==, <, <=, >, >= involving NA compare False, while != compares True — so a boolean mask is always pure bool, clean for df[mask]. Note the != exception: s != value therefore includes missing rows.

pandas interop

pandas is not a runtime dependency; these bridges import it lazily, only when called, so import volas stays pandas-free.

from volas import DataFrame

df = DataFrame.from_pandas(pandas_df)  # numeric / bool / str / datetime native; a (tz-aware) DatetimeIndex round-trips;
                                   # a nullable Int64 / boolean / string column reads back as int / bool / str + volas.NA
pdf = df.to_pandas()               # -> pandas.DataFrame ('numpy' backend: an int/bool column with NA becomes float64 + NaN)
pdf = df.to_pandas(dtype_backend='numpy_nullable')  # faithful masked Int64 / boolean (a lossless NA round-trip)
df.to_csv('out.csv', index=True)   # subset of pandas to_csv; returns a str if path=None

to_pandas's dtype_backend ('numpy' vs the lossless 'numpy_nullable') governs how a missing value crosses into pandas — see df.to_pandas for the per-value breakdown.

Arrow & DLPack interop (zero-copy)

A volas column owns one contiguous buffer per dtype (Arrow-native string layout included), so it crosses to Arrow and DLPack consumers without a copy — the consumer borrows the same bytes, kept alive by volas.

import pyarrow as pa, numpy as np

# Arrow C-Data / C-Stream — pyarrow, polars, … read volas directly via the
# standard PyCapsule protocols (__arrow_c_array__ / __arrow_c_stream__).
pa.array(df['close'])              # Series  -> pyarrow.Array  (shares the buffer)
pa.table(df)                       # DataFrame -> pyarrow.Table (one RecordBatch)
df['close'].to_arrow()             # convenience for pa.array(...)

Series.from_arrow(pa_array, name='close')   # Arrow array  -> Series (borrowed where dtypes match)
DataFrame.from_arrow(pa_table)              # Arrow table  -> DataFrame

# DLPack — NumPy / PyTorch / JAX borrow a dense numeric (or bool) column.
np.from_dlpack(df['close'])        # zero-copy ndarray view

The high-level entry points — df.to_arrow / DataFrame.from_arrow, series.to_arrow / Series.from_arrow — are documented under Usage. They sit on these standard protocol methods, which Arrow / array consumers call automatically (so you rarely call them yourself):

• Series.__arrow_c_array__ — the Arrow C-Data array protocol; returns the (schema, array) PyCapsule pair, so pa.array(s) / pl.Series(s) read a column.
• Series.__arrow_c_schema__ — the schema-only half (the column's Arrow dtype).
• DataFrame.__arrow_c_stream__ — the Arrow C-Stream protocol; the frame as one RecordBatch, so pa.table(df) / pl.from_dataframe(df) read a frame.
• Series.__dlpack__ / Series.__dlpack_device__ — the DLPack protocol, so np.from_dlpack(s) / torch.from_dlpack(s) borrow a dense numeric / bool column. The borrowed view is read-only (a versioned-DLPack flag) so writing through it can't bypass copy-on-write and corrupt the frame; a pre-1.0 consumer that cannot receive the flag is given an independent copy instead. np.from_dlpack(s, copy=True) returns an independent writable copy. A non-CPU device or a stream is refused (BufferError); an int/bool column with a missing value (DLPack has no null mask) or a str/datetime column raises.

Zero-copy contract. The data buffer is shared (no copy) in both directions for volas's native numeric (int32 / int64 / float32 / float64), string, and nanosecond-datetime columns. The small repacks are:

• bool — volas stores one byte per value, Arrow one bit;
• the null bitmap (≤ n/8 bytes) — including, on export, a one-pass scan that turns a missing float (in-band NaN) into a real Arrow null;
• on import only: a 32-bit-offset Utf8 (widened to 64-bit offsets) or a string_view (materialised to contiguous bytes), a coarser-than-ns timestamp (rescaled), a Decimal128 / Decimal256 (→ f64, lossy past ~15 digits — keep prices as strings for exactness), a narrow / unsigned integer (→ i64; a UInt64 past i64::MAX has no lossless image and raises), a dictionary / categorical column (decoded to its values), a typeless Null column (→ an all-NA f64 column), or a Date32 / Date64 (→ ns datetime). Other Arrow types raise a clear error.

NA at the boundary. Every missing value — int, bool, and float — crosses as a real Arrow null (a missing float is the in-band NaN, scanned into the null bitmap on export), so a downstream is_null / null_count sees them all consistently. DLPack and an integer to_numpy have no null channel, so they raise on a missing value — pass na_value=, or use the Arrow path, which carries the null bitmap losslessly:

df['qty'].to_numpy(dtype='int64')                # raises if any value is NA (pandas-aligned)
df['qty'].to_numpy(dtype='int64', na_value=0)    # or fill the holes with na_value
pa.array(df['qty'])                              # lossless: keeps int64 + the null bitmap

Timezone. Arrow tables have no index, so the frame's index — and its timezone — is not carried: a datetime column crosses as naive UTC nanoseconds (the absolute instant is exact; only the display-zone label is dropped), and from_arrow returns a fresh RangeIndex. Re-apply a zone after import with tz_localize / tz_convert.

Error handling

Directive problems raise typed exceptions. Both subclass DirectiveError and the built-in ValueError, so existing except ValueError handling keeps working.

from volas import DirectiveSyntaxError, DirectiveValueError

try:
    df['ma:2,3']                 # too many arguments
except DirectiveValueError as e:
    ...                          # unknown command/sub-command, bad arg, bad value

try:
    df['a >']                    # malformed expression
except DirectiveSyntaxError as e:
    ...                          # message carries the line / column of the error

Built-in Indicators

The complete directive reference lives in INDICATORS.md. It covers Volas-exclusive indicators, built-in statistical commands, and TA-Lib-compatible directives.

Contributing & feedback

Issues, indicator requests, benchmark challenges, and PRs are welcome — see CONTRIBUTING.md and start a thread in Discussions. The most useful feedback is on the API surface and the benchmark methodology.

If you build live OHLCV / technical-indicator pipelines in Python, star the repo to follow new indicators, benchmark results, and releases.

License

MIT

For Developers

Developer notes, local build commands, dependency groups, and benchmark report guidance live in DEVELOPMENT.md.