🚀 Wingfoil

Wingfoil is a blazingly fast, highly scalable stream processing framework designed for latency-critical use cases such as electronic trading and real-time AI systems. You define a graph of transformations over streams; Wingfoil drives their execution in a tightly scheduled DAG, either against live data or replayed history.

The Rust engine does the heavy lifting; this wingfoil package gives you the same graph model, operators, and production-ready I/O adapters from Python.

Features
Installation
Quick Start
Core Concepts
Stream Operators
Composing Streams: Graph, bimap, CustomStream
Backtesting with Historical Mode
Pandas Integration
I/O Adapters
Build from Source
Release Status & Feedback

✨ Features

Fast — ultra-low latency and high throughput with an efficient DAG execution engine written in Rust.
Simple and obvious — define your graph with fluent operators; Wingfoil manages scheduling and data propagation.
Backtesting out of the box — switch from real-time to historical replay by flipping a single flag.
Production I/O adapters — CSV, KDB+, etcd, ZeroMQ, iceoryx2, FIX 4.4 (incl. TLS), Prometheus, and OTLP — ready to plug into your graph.
Multi-language — Rust crate and Python package today, WASM/JS/TS planned.

📦 Installation

pip install wingfoil

Wingfoil wheels are published for Linux, macOS, and Windows on CPython 3.8+.

Optional adapters require the matching server/library (KDB+, etcd, iceoryx2, a FIX counterparty, OTLP collector, etc.) but no additional Python dependencies — the adapter clients are compiled into the wheel.

⚡ Quick Start

from wingfoil import ticker

(
    ticker(1.0)                       # tick every second
        .count()                      # 1, 2, 3, ...
        .map(lambda n: f"hello, world {n}")
        .logged(">>")                 # INFO-log each value
        .run(realtime=True, duration=3.0)
)

[INFO wingfoil] 0.000_092 >> hello, world 1
[INFO wingfoil] 1.008_038 >> hello, world 2
[INFO wingfoil] 2.012_219 >> hello, world 3

run() blocks until the stop condition is reached. Pass any of:

Argument	Type	Meaning
`realtime`	`bool`	`True` uses wall-clock time; `False` is historical replay.
`start`	`float` \| `datetime`	Historical start (Unix-seconds float or UTC `datetime`).
`duration`	`float` \| `timedelta`	Stop after this many seconds of graph time.
`cycles`	`int`	Stop after this many engine cycles.

🧠 Core Concepts

Stream — a time-stamped channel of values. Streams are produced by sources (ticker, constant, I/O adapters) and transformed with operators (.map, .filter, .distinct, ...). Every operator returns a new Stream.
Node — anything schedulable. A Stream is a Node that also carries a value; pure side-effect sinks (.for_each, .csv_write, .zmq_pub, ...) return a Node.
Graph — a bundle of roots that share one engine run. Use Graph([...]) when you have several independent stream branches that must execute together (e.g. publisher + subscriber + monitoring).
Active vs. passive upstreams — an active upstream triggers downstream execution on tick; a passive upstream is read but does not trigger. Most built-in operators use active inputs; .sample(trigger) is the common way to fire a stream from a different clock.

Run Modes

realtime=True — the engine tracks wall-clock time. Use with live inputs (sockets, brokers, iceoryx2, etc.).
realtime=False — historical replay, driven by event timestamps. Ideal for backtests and deterministic tests. In historical mode the graph runs as fast as the CPU allows; time advances purely from source events.

🧰 Stream Operators

All methods are available on Stream instances. Examples assume from wingfoil import ticker, constant, bimap, Graph.

Source operators

Operator	Description
`ticker(period)`	Emit once every `period` seconds. Returns a `Node`.
`constant(value)`	Emit `value` once on the first cycle.

Transforming values

Operator	Description
`.map(f)`	Apply `f(value)` to each tick.
`.filter(pred)`	Drop values where `pred(value)` is false.
`.distinct()`	Drop consecutive duplicates.
`.difference()`	Emit `current - previous`.
`.delay(seconds)`	Replay values delayed by `seconds`.
`getattr(s, 'not')()`	Logical/arithmetic negation (the literal method name is `not`; invoke via `getattr` because `not` is a Python keyword).
`.limit(n)`	Pass through at most `n` values, then stop.
`.sample(trigger)`	Re-emit the current value on each `trigger` tick.

Aggregation

Operator	Description
`.count()`	Emit tick count: 1, 2, 3, ...
`.sum()`	Running sum (values must be numeric).
`.average()`	Running mean (values must be numeric).
`.buffer(n)`	Tumbling window of size `n`.
`.collect()`	Accumulate every value into a `list` emitted each cycle.
`.with_time()`	Pair each value with graph-time as `(seconds, value)`.
`.dataframe()`	Collect `[(time, value), ...]` for pandas (see below).

Observing and sinking

Operator	Description
`.inspect(f)`	Call `f(value)` and pass the value through.
`.logged("label")`	`INFO`-log each value and pass it through.
`.for_each(f)`	Terminal sink: `f(value, time)` on every tick.
`getattr(s, 'finally')(f)`	Terminal sink: `f(final_value)` called once at shutdown (literal method name `finally` collides with Python's keyword, so use `getattr`).
`.peek_value()`	After `run()`, inspect the last emitted value.

Execution

Operator	Description
`.run(realtime, start=, duration=, cycles=)`	Build and run a one-root graph.
`Graph([...]).run(...)`	Build and run a multi-root graph.

Example: most operators in one pipeline

from wingfoil import ticker

avg_of_odds = (
    ticker(0.1)
        .count()
        .filter(lambda x: x % 2 == 1)   # 1, 3, 5, ...
        .map(float)
        .average()                      # running mean
        .logged("avg")
)

avg_of_odds.run(realtime=False, cycles=10)
print("last:", avg_of_odds.peek_value())

🧱 Composing Streams: `Graph`, `bimap`, `CustomStream`

`Graph` — run several roots together

from wingfoil import ticker, Graph

quotes = ticker(0.1).count().map(lambda i: 100 + i).logged("quote")
heartbeat = ticker(1.0).count().logged("heartbeat")

Graph([quotes, heartbeat]).run(realtime=True, duration=2.5)

`bimap` — fuse two streams

from wingfoil import ticker, constant, bimap

a = ticker(0.1).count()                       # 1, 2, 3, ...
b = constant(0.5).sample(ticker(0.1))         # 0.5 on every tick

(bimap(a, b, lambda x, y: x + y)
    .logged("sum")
    .run(realtime=False, cycles=5))

`CustomStream` — write your own operator in Python

Subclass CustomStream and implement cycle():

import math
from wingfoil import ticker, CustomStream

class Polynomial(CustomStream):
    """Sum of upstream[i] * 10**i."""

    def cycle(self):
        value = sum(
            src.peek_value() * math.pow(10, i)
            for i, src in enumerate(self.upstreams())
        )
        self.set_value(value)
        return True

source = ticker(0.1).count()
(
    Polynomial([source] * 3)
        .map(lambda x: x * 0.01)
        .logged("poly")
        .run(realtime=False, cycles=5)
)

🕰️ Backtesting with Historical Mode

Pass realtime=False to drive the graph from source timestamps rather than the wall clock. Add start= if your sources require a specific epoch start (such as kdb_read), and cap the replay with duration= or cycles=.

from datetime import datetime, timezone
from wingfoil import ticker

stream = ticker(0.01).count().collect()
stream.run(
    realtime=False,
    start=datetime(2025, 1, 1, tzinfo=timezone.utc),
    cycles=5,
)
print(stream.peek_value())   # [1, 2, 3, 4, 5]

Historical mode is deterministic — it's the right mode for unit tests and strategy backtests.

🐼 Pandas Integration

wingfoil ships with two pandas helpers:

stream.dataframe() — collects (time, value) pairs into a list; combine with wingfoil.to_dataframe to materialise a pandas.DataFrame.
wingfoil.build_dataframe({"col": stream, ...}) — aligns several .dataframe() streams by graph time.

from wingfoil import ticker, Graph, build_dataframe

source = ticker(0.01).count().limit(5)
prices = source.map(lambda i: 100 + i).dataframe()
quantities = source.map(lambda _: 10).dataframe()

Graph([prices, quantities]).run(realtime=False)

df = build_dataframe({"price": prices, "qty": quantities})
print(df)
#       time  price  qty
# 0  0.0e+00    101   10
# 1  1.0e-02    102   10
# ...

A single-stream variant using to_dataframe:

from wingfoil import ticker, to_dataframe

stream = (
    ticker(0.01)
        .count()
        .limit(5)
        .map(lambda i: {"price": 100 + i, "qty": 10})
        .dataframe()
)
stream.run(realtime=False)
df = to_dataframe(stream.peek_value())
print(df)

🔌 I/O Adapters

All adapters are exposed from the top-level wingfoil module. Every write method (csv_write, kdb_write, etcd_pub, zmq_pub, iceoryx2_pub, otlp_push) returns a Node — drive it by calling .run(...).

CSV

Read a CSV file into a stream of dicts (keys = column headers, values = strings). The file must have a header row and a timestamp column encoded as integer nanoseconds since the Unix epoch.

from wingfoil import csv_read

rows = csv_read("prices.csv", time_column="time_ns").collect()
rows.run(realtime=False)
print(rows.peek_value())         # [{'time_ns': '...', 'sym': 'AAPL', ...}, ...]

Write a stream of dicts to CSV. Headers are inferred from the first dict; a time column with graph-time nanoseconds is prepended automatically.

from wingfoil import ticker

(
    ticker(0.1)
        .count()
        .limit(5)
        .map(lambda i: {"sym": "AAPL", "price": 100.0 + i})
        .csv_write("out.csv")
        .run(realtime=False)
)

KDB+

Start a q process (q -p 5000) and create the target table:

test_trades:([]time:`timestamp$();sym:`symbol$();price:`float$();qty:`long$())

from wingfoil import ticker, kdb_read

HOST, PORT, TABLE = "localhost", 5000, "test_trades"

# Write: each dict becomes one row; "columns" names the non-time columns.
(
    ticker(1.0).count().limit(10)
        .map(lambda i: {"sym": "AAPL", "price": 100.0 + i, "qty": i * 10 + 1})
        .kdb_write(
            host=HOST, port=PORT, table=TABLE,
            columns=[("sym", "symbol"), ("price", "float"), ("qty", "long")],
        )
        .run(realtime=False)
)

# Read: time-sliced query; returns a stream of dicts.
# `start` and `duration` bound the replay window against the KDB time column.
rows = kdb_read(
    host=HOST, port=PORT,
    query=f"select from {TABLE}",
    time_col="time",
    chunk_size=10_000,
).collect()
rows.run(realtime=False, start=946684800.0, duration=86400.0)
print(rows.peek_value())

Supported kdb_write column types: "symbol", "float", "long", "int", "bool".

etcd

Start etcd (docker run --rm -p 2379:2379 gcr.io/etcd-development/etcd:v3.5.0).

from wingfoil import ticker, etcd_sub

ENDPOINT = "http://localhost:2379"

# Publish: each dict = {"key": str, "value": bytes}, or a list of them per tick.
(
    ticker(1.0).count().limit(3)
        .map(lambda i: {"key": f"/wf/item/{i}", "value": str(i).encode()})
        .etcd_pub(ENDPOINT, lease_ttl=30.0, force=True)
        .run(realtime=True)
)

# Subscribe: snapshot + watch events under a prefix; each tick = list[event].
events = etcd_sub(ENDPOINT, "/wf/").inspect(print)
events.run(realtime=True, duration=2.0)

Event dicts have shape: {"kind": "put"|"delete", "key": str, "value": bytes, "revision": int}.

ZeroMQ

Cross-language compatible — the Rust publisher/subscriber inter-operate with Python on both sides.

Direct mode — hard-coded address, no discovery infrastructure:

# zmq_pub.py
import wingfoil as wf

(
    wf.ticker(0.5).count()
        .map(lambda n: str(n).encode())
        .zmq_pub(port=7779)
        .run(realtime=True)
)

# zmq_sub.py
import wingfoil as wf

data, status = wf.zmq_sub("tcp://127.0.0.1:7779")
data_node = data.inspect(lambda msgs: [print("msg:", m) for m in msgs])
status_node = status.inspect(lambda s: print("status:", s))
wf.Graph([data_node, status_node]).run(realtime=True)

zmq_sub returns (data_stream, status_stream). Each data_stream tick yields list[bytes] of messages received that cycle. status_stream yields "connected" / "disconnected".

etcd discovery — publishers register under a service name; subscribers look it up. Useful for dynamic deployments. Requires a running etcd.

# publisher
wf.ticker(0.5).count().map(lambda n: str(n).encode()) \
    .zmq_pub_etcd("quotes", 7779, "http://127.0.0.1:2379") \
    .run(realtime=True)

# subscriber
data, status = wf.zmq_sub_etcd("quotes", "http://127.0.0.1:2379")

For multi-host deployments where 127.0.0.1 isn't routable, use zmq_pub_etcd_on(name, address, port, endpoint).

iceoryx2 (shared memory)

Zero-copy pub/sub over shared memory. Requires building wingfoil with the iceoryx2 feature (opt-in; see Build from Source).

from wingfoil import ticker, iceoryx2_sub, Iceoryx2ServiceVariant, Iceoryx2Mode, Graph

service = "wingfoil/demo"

sub = iceoryx2_sub(
    service,
    variant=Iceoryx2ServiceVariant.Local,   # or Ipc
    mode=Iceoryx2Mode.Signaled,             # Spin | Threaded | Signaled
)
sub = sub.inspect(lambda msgs: print("received:", msgs)).collect()

pub = (
    ticker(0.1).count()
        .map(lambda n: f"tick {n}".encode())
        .iceoryx2_pub(service, variant=Iceoryx2ServiceVariant.Local)
)

Graph([pub, sub]).run(realtime=True, duration=0.5)

Both ends accept variant (Ipc for cross-process, Local for same-process), history_size, and publisher-side initial_max_slice_len.

FIX protocol

FIX 4.4 initiator, TLS initiator, and acceptor. All return (data_stream, status_stream); TLS additionally returns a sender object for sending outbound messages.

from wingfoil import fix_connect

data, status = fix_connect(
    host="fix.example.com",
    port=9876,
    sender_comp_id="MYCOMP",
    target_comp_id="BROKER",
)

messages = data.inspect(lambda msgs: [print("fix:", m) for m in msgs])
states = status.inspect(lambda ss: [print("session:", s) for s in ss])

import wingfoil as wf
wf.Graph([messages, states]).run(realtime=True, duration=10.0)

Each data tick yields a list[dict] where every dict is {"msg_type": str, "seq_num": int, "fields": [(tag, value), ...]}. Status values are "disconnected" | "logging_in" | "logged_in" or a dict {"status": "logged_out"|"error", "reason"|"message": str}.

TLS initiator (e.g. LMAX) with a sender:

from wingfoil import fix_connect_tls

data, status, sender = fix_connect_tls(
    host="fix-marketdata.london-digital.lmax.com",
    port=443,
    sender_comp_id="USERNAME",
    target_comp_id="LMXBL",
    password="secret",
)

# Send a FIX message on the session:
sender.send({
    "msg_type": "V",
    "fields": [(262, "req1"), (263, "1"), (264, "0")],
})

Acceptor:

from wingfoil import fix_accept

data, status = fix_accept(port=9876, sender_comp_id="MYCOMP", target_comp_id="INIT")

Prometheus

Expose any stream as a gauge metric on a Prometheus-compatible /metrics endpoint.

from wingfoil import ticker, Graph, PrometheusExporter

exporter = PrometheusExporter("0.0.0.0:9091")
exporter.serve()                              # bind and start the HTTP server

tick_count = ticker(1.0).count()
requests_count = ticker(0.1).count()

Graph([
    exporter.register("tick_count", tick_count),
    exporter.register("requests_count", requests_count),
]).run(realtime=True, duration=5.0)

Scrape with curl http://localhost:9091/metrics.

OpenTelemetry OTLP

Push any stream's value to an OTLP HTTP collector as a gauge metric.

from wingfoil import ticker

(
    ticker(1.0).count()
        .otlp_push(
            metric_name="wingfoil_ticks",
            endpoint="http://localhost:4318",
            service_name="demo",
        )
        .run(realtime=True, duration=10.0)
)

🛠️ Build from Source

Most users should pip install wingfoil. To build locally (e.g. to enable the iceoryx2 feature or develop against the bindings), see build.md.

git clone https://github.com/wingfoil-io/wingfoil
cd wingfoil/wingfoil-python
pip install maturin
maturin develop                               # or: maturin develop --features iceoryx2
pytest

📢 Release Status & Feedback

The Wingfoil Python module is currently a beta release. APIs are stabilising and we would love your input — especially if you:

are interested in contributing,
know of a project Wingfoil is a good fit for,
want to request a feature, or
have any feedback.

Email us at hello@wingfoil.io, open a GitHub discussion, or browse the issue tracker.

More resources:

📚 Python examples: https://github.com/wingfoil-io/wingfoil/tree/main/wingfoil-python/examples
🦀 Rust crate docs: https://docs.rs/wingfoil

wingfoil-python 6.0.3

🚀 Wingfoil

Table of Contents

✨ Features

📦 Installation

⚡ Quick Start

🧠 Core Concepts

Run Modes

🧰 Stream Operators

Source operators

Transforming values

Aggregation

Observing and sinking

Execution

Example: most operators in one pipeline

🧱 Composing Streams: `Graph`, `bimap`, `CustomStream`

`Graph` — run several roots together

`bimap` — fuse two streams

`CustomStream` — write your own operator in Python

🕰️ Backtesting with Historical Mode

🐼 Pandas Integration

🔌 I/O Adapters

CSV

KDB+

etcd

ZeroMQ

iceoryx2 (shared memory)

FIX protocol

Prometheus

OpenTelemetry OTLP

🛠️ Build from Source

📢 Release Status & Feedback

wingfoil-python 6.0.3

🚀 Wingfoil

Table of Contents

✨ Features

📦 Installation

⚡ Quick Start

🧠 Core Concepts

Run Modes

🧰 Stream Operators

Source operators

Transforming values

Aggregation

Observing and sinking

Execution

Example: most operators in one pipeline

🧱 Composing Streams: Graph, bimap, CustomStream

Graph — run several roots together

bimap — fuse two streams

CustomStream — write your own operator in Python

🕰️ Backtesting with Historical Mode

🐼 Pandas Integration

🔌 I/O Adapters

CSV

KDB+

etcd

ZeroMQ

iceoryx2 (shared memory)

FIX protocol

Prometheus

OpenTelemetry OTLP

🛠️ Build from Source

📢 Release Status & Feedback

🧱 Composing Streams: `Graph`, `bimap`, `CustomStream`

`Graph` — run several roots together

`bimap` — fuse two streams

`CustomStream` — write your own operator in Python