fugazi (Python)
Python bindings for fugazi, a library of incremental,
composable technical-analysis primitives.
- Incremental — every indicator and signal carries its own state and is
advanced one sample at a time with
update(), in ~O(1) and with no full-history recomputation. The same object serves live streaming and batch backtesting. - Composable — indicators own their input source, so you build complex
indicators and signals by nesting constructors. There is no pipe or glue
step: an "EMA of an SMA of the close" is literally
ta.ema(ta.sma(ta.close(), 10), 20), and a trade condition is a single object you can feed bars.
Install
pip install fugazi
Then import fugazi. Prebuilt wheels are published for Linux, macOS
(Intel + Apple Silicon) and Windows.
To build from a checkout instead (for development):
pip install maturin
maturin develop --release # editable install into the active virtualenv
Quick start
You build indicators by nesting constructors. Every indicator is rooted at
a leaf source — usually a candle field (close(), high(), volume(), ...):
import fugazi as ta
ema = ta.ema(ta.close(), 20) # EMA-20 of the close
node = ta.ema(ta.sma(ta.close(), 10), 20) # EMA-20 of an SMA-10 — just keep nesting
The root decides what the indicator consumes. A candle-rooted indicator takes
Candles (any of OHLCV); to work on a bare stream of numbers instead, root
it at identity() — the leaf that passes raw values straight through:
prices = ta.rsi(ta.identity(), 14) # RSI of a plain float series
Then drive it one of two ways: streaming (a bar at a time) or batch (a whole series at once). They share the same indicators; pick by how your data arrives.
What you feed update()/feed() follows from the root: a candle-rooted
indicator consumes candles, an identity()-rooted one consumes plain
numbers.
Streaming API — one sample at a time
Feed one sample to update(); it returns a float, or None until warmed up.
This is the live/incremental path. Every node also has value() (or is_true() for a boolean Signal),
is_ready(), and reset().
node = ta.ema(ta.sma(ta.close(), 10), 20) # candle-rooted
for o, h, l, c, v in bars:
value = node.update(ta.Candle(o, h, l, c, v)) # feed a Candle -> float | None
print(value)
prices = ta.rsi(ta.identity(), 14) # identity-rooted
for px in [100.0, 101.5, 100.8]:
prices.update(px) # feed a float
Batch API — a whole series at once
feed(data) computes every bar in one call. For a candle-rooted indicator,
data is a dataframe with OHLCV columns — pandas and polars both work (also
a dict of columns) — and only the columns an indicator needs have to be
present:
import pandas as pd # or: import polars as pl
# df is your OHLCV frame (open/high/low/close/volume columns)
df["ema20"] = ta.ema(ta.close(), 20).feed(df) # assigns straight back
ta.atr(14).feed(df) # uses high/low/close
ta.vwap().feed(df) # uses high/low/close/volume
Column names are matched case-insensitively (Close/CLOSE/close), and
close is required. An identity()-rooted indicator instead takes a plain
1-D series — a list, NumPy array, or pandas/polars Series:
ta.ema(ta.identity(), 20).feed([100.0, 101.5, 100.8, 102.3, 101.9])
ta.ema(ta.identity(), 20).feed(df["close"])
(The root is the contract: a candle indicator won't silently treat a bare array as the close, and a value indicator won't accept a frame — pick the root that matches your data.)
The output mirrors the input library, one value per bar, with warm-up bars
as NaN (so the result lines up with your rows and assigns straight back):
| Input | Indicator | Multi-line (macd, bollinger, …) | Signal |
|---|---|---|---|
| pandas | Series (index preserved) |
DataFrame (one column per line) |
bool Series |
| polars | Series |
DataFrame |
bool Series |
| list / dict / NumPy | ndarray |
dict of ndarrays |
bool ndarray |
ta.ema(ta.close(), 20).feed(df) # pandas Series, df.index
ta.macd(ta.close()).feed(df) # pandas DataFrame: macd/signal/histogram
ta.macd(ta.identity()).feed(prices_list) # {"macd": ndarray, "signal": ndarray, ...}
(If NumPy isn't installed, list/dict input falls back to plain Python lists.)
feed is itself incremental — it just loops update over the batch through
the node's own state and never auto-resets. So calling it on successive chunks
continues the same stream: the warm-up is paid once, and the concatenated
outputs equal a single feed over the whole series. This is what lets you process
data as it arrives without recomputing history:
node = ta.sma(ta.identity(), 3)
x1 = node.feed(series1) # warms up, emits for series1
x2 = node.feed(series2) # continues from where series1 left off
# np.concatenate([x1, x2]) == ta.sma(ta.identity(), 3).feed(series1 + series2)
node.reset() # call reset() to start a fresh, independent pass
A source can be reused after you pass it into a constructor:
src = ta.close() fast = ta.ema(src, 10) slow = ta.ema(src, 20) # `src` is still usable here
Indicators
| Constructor | Output |
|---|---|
open() high() low() close() volume() typical() median() |
the candle field |
identity() |
the raw value stream (root for a bare numeric series) |
value(x) |
a constant |
sma ema rma wma hma rsi stddev stochastic cci (source, period) |
a value |
stoch_rsi(source, rsi_period=14, stoch_period=14) |
a value |
atr mfi williams_r (period) |
a value |
obv() vwap() ad() true_range() |
a value |
sar(step=0.02, max=0.2) |
a value |
macd(source, fast=12, slow=26, signal=9) |
dict {macd, signal, histogram} |
bollinger(source, period=20, k=2.0) |
dict {upper, middle, lower} |
keltner(source, ema_period=20, atr_period=10, multiplier=2.0) |
dict {upper, middle, lower} |
donchian(high, low, period) |
dict {upper, middle, lower} |
adx(period) |
dict {plus_di, minus_di, adx} |
dmi(period) |
dict {plus_di, minus_di} |
aroon(period) |
dict {up, down, oscillator} |
Multi-line indicators return a dict of their named lines (or None while
warming up).
Operators
Combine value indicators into other indicators:
ta.close().add(other) # also: sub, mul, div — or the + - * / operators
ta.close().lag(1) # also: diff, ratio, roc
ta.close().rolling_max(20) # also: rolling_min
...or into signals (booleans):
fast.gt(slow) # also: lt, ge, le, eq, ne (optional epsilon=...)
ta.rsi(ta.close(), 14).above(70.0) # also: below(level)
fast.crosses_above(slow) # also: crosses_below
Signals compose with each other and update to a bool:
sig = a.and_(b) # also: or_, xor_, not_(), changed() — or a & b | ~c
sig.update(candle) # -> bool
Example
"Fast EMA crosses above slow EMA while RSI is not already overbought" — one signal, usable either way:
import fugazi as ta
def golden():
return (
ta.ema(ta.close(), 12)
.crosses_above(ta.ema(ta.close(), 26))
.and_(ta.rsi(ta.close(), 14).below(70.0))
)
# streaming: react bar by bar
signal = golden()
for bar in stream:
if signal.update(bar):
print("entry signal")
# batch: a boolean Series/array over the whole frame
entries = golden().feed(df)
Trading: the wallet
The strategy layer is exposed as a wallet you trade into. There is no
strategy class to subclass — a "strategy" in Python is just your own code that,
each bar, reads signals and calls wallet methods. PaperWallet is the built-in,
in-memory book (funds + positions + a trade blotter); live execution belongs in
your own code, not here.
import fugazi as ta
wallet = ta.PaperWallet(10_000.0) # seed with cash
wallet.update("AAPL", 185.0) # feed the price each tick (before trading)
# set: absolute target (opposite side reverses) · set_position: absolute units · close: flat
wallet.set("AAPL", "buy", 10) # target 10 units (a number = units)
wallet.set("AAPL", "buy", ta.Size.value_frac(0.25)) # target 25% of equity
wallet.set("AAPL", "buy", ta.Size.position_frac(0.5)) # trim to 50% of the position
wallet.set_position("AAPL", 4) # drive straight to 4 units
wallet.close("AAPL") # flatten
wallet.funds # cash balance
wallet.position("AAPL") # signed position (negative = short)
wallet.price("AAPL") # last fed price (or None)
wallet.positions() # {symbol: units}
wallet.equity() # funds + positions marked at the fed prices
wallet.orders() # the blotter: list of Order(symbol, side, units)
The wallet is fed each symbol's price with update(symbol, price) and is
otherwise market-agnostic. Sizes are an absolute number of units, or
ta.Size.funds_frac(f) (cash) / ta.Size.value_frac(f) (equity; 1.0 is
all-in) / ta.Size.position_frac(f); sides are "buy"/"sell". A movement that
can't be carried out — no/zero price fed, or a buy beyond available funds —
raises ValueError. A full strategy loop — price the wallet, advance every
signal each bar, then act:
enter = ta.sma(ta.close(), 3).crosses_above(ta.sma(ta.close(), 10))
exit_ = ta.sma(ta.close(), 3).crosses_below(ta.sma(ta.close(), 10))
wallet = ta.PaperWallet(10_000.0)
for o, h, l, c, v in bars:
candle = ta.Candle(o, h, l, c, v)
wallet.update("AAPL", c) # price the wallet
went_long, went_flat = enter.update(candle), exit_.update(candle)
if went_long:
wallet.set("AAPL", "buy", ta.Size.value_frac(1.0)) # all-in long
elif went_flat:
wallet.close("AAPL")
1 maintainer
Metadata
1 maintainer
Release files for fugazi 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| fugazi-0.4.0.tar.gz | 196.2 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| fugazi-0.4.0-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| fugazi-0.4.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| fugazi-0.4.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| fugazi-0.4.0-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
| fugazi-0.4.0-cp39-abi3-macosx_10_12_x86_64.whl | CPython 3.9 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 2.1 MB
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