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):

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

Multi-line indicators return a dict of their named lines (or None while warming up).

Projecting one line of a multi-output indicator: shared()

Call .shared() on any multi-output indicator (macd, bollinger, adx, donchian, keltner, dmi, aroon) to get a handle whose per-line accessors return ordinary Indicators that compose with the usual operators (gt, crosses_above, add, …). Every accessor built off one .shared() handle projects into the same underlying source — the multi advances at most once per bar however many accessors read out of it, exactly like Rust's Macd::new(...).shared():

# MACD line crossing its signal line, as a single composed Signal:
macd = ta.macd(ta.close(), 12, 26, 9).shared()
bullish = macd.line().crosses_above(macd.signal())

# Close pierces the Bollinger upper band:
bands = ta.bollinger(ta.close(), 20, 2.0).shared()
breakout = ta.close().gt(bands.upper())

The accessor names mirror the Rust API: line()/signal()/histogram() on a MACD, upper()/middle()/lower() on Bollinger/Keltner/Donchian, plus_di()/minus_di()/adx() on ADX/DMI, up()/down()/oscillator() on Aroon. component(name) is a programmatic fallback, names() lists what's available for a given handle. Calling .shared() returns a fresh handle owning its own copy of the source, so the original MultiIndicator (with its dict- returning .update() / .feed() API) stays usable in parallel.

Cross-timeframe composition

resample + latch compose a higher-timeframe pipeline over a base candle stream: resample(N, inner) aggregates every N base candles into one HTF candle and runs inner (any candle-rooted Real source — close(), ema(close(), 20), …) over it, emitting inner's output on the completing tick and None in between. The resample's clock stays base-timeframe: it's fed one base candle per update() and reports at that same cadence — the emitted output marks whether the inner produced a value on a completed bucket. Wrap the whole resample in latch() so per-base-tick reads see the finished value between boundaries.

# EMA-20 of the closes of every 4-bar candle, latched for per-base-tick reads.
htf_ema = ta.latch(ta.resample(4, ta.ema(ta.close(), 20)))

The only correct ordering is resample(N, ema(...)) — with the recursive smoother as the resample's inner — then latch on the outside; latching before the recursive smoother would feed it a held (repeated) value on every base tick, distorting the recurrence.

stable(signal) composes with the same signal in an and_ to gate a strategy's entry on its own sources being past their unstable tail. It's also available as a method on any Indicator or Signal:

entry = ta.close().crosses_above(ta.ema(ta.close(), 20))
gated = entry.and_(entry.stable())          # method form
gated = entry.and_(ta.stable(entry))        # equivalent free-function form
warm = ta.ema(ta.close(), 20).stable()      # Indicator.stable() -> Signal

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")

Metrics

fugazi.metrics is the standalone reporting surface — one function per metric so you pick only what you need. Return moments (mean_return, stddev_return, skewness, value_at_risk, …), risk-adjusted ratios (sharpe, sortino, calmar, omega, ulcer_performance_index), drawdown analytics (max_drawdown, average_drawdown, time_in_drawdown_ratio, recovery_factor), and round-trip trade statistics (win_rate, profit_factor, expectancy, kelly_fraction, average_bars_held, …) are all there. Values are in natural units — 0.15 is +15%, not 15.0 — and ratios that can vanish (zero variance for Sharpe, no losing trade for a profit factor, non-positive endpoints for CAGR) return None rather than NaN.

Three intermediate builders — per_bar_returns, reconstruct_trades, drawdown_segments — turn the equity curve and fill blotter into what the metric functions consume, so a caller computing several metrics builds each intermediate once:

from fugazi import metrics

equity = [10_000.0, 10_050.0, 10_100.0, 9_900.0, 10_200.0, 10_300.0]
returns = metrics.per_bar_returns(equity, initial_equity=10_000.0)

metrics.sharpe(returns, risk_free_rate=0.0, bars_per_year=252)   # ratio | None
metrics.total_return(equity, initial_equity=10_000.0)            # 0.03
metrics.max_drawdown(metrics.drawdown_segments(equity))          # fraction

reconstruct_trades walks a bar-tagged fill blotter with a signed position and a volume-weighted entry, producing one Trade per closed leg. Since PaperWallet.update() returns bare Orders (no bar), tag each with the bar you're on using fugazi.Fill(bar, order) as you drive the loop:

from fugazi import metrics

fills = []
wallet = ta.PaperWallet(10_000.0)
wallet.set_position("AAPL", 100.0)         # queued market buy
for i, c in enumerate(candles):
    for order in wallet.update("AAPL", c):
        fills.append(ta.Fill(bar=i, order=order))

trades = metrics.reconstruct_trades(fills)
metrics.win_rate(trades)                   # win fraction | None
metrics.profit_factor(trades)              # Σwins / |Σlosses| | None
metrics.exposure_ratio(fills, total_bars=len(candles))

Fetching data

Two remote candle providers ship built in — Binance (crypto spot klines) and Yahoo (stocks, ETFs, indices, FX). Each is a client class with one method, candles(...), returning a polars/pandas DataFrame (or a dict of lists with output="numpy"):

import fugazi as ta

binance = ta.Binance()                     # public endpoint, defaults
df = binance.candles(symbol="BTCUSDT", freq="1d",
                     since="2020-01-01", until="today")

yahoo = ta.Yahoo()
df = yahoo.candles(symbol="AAPL", freq="1d", since="2020-01-01")

freq is a bar-cadence token ("1m"/"5m"/"1h"/"4h"/"1d"/"1w"/"1M"); since/until accept ISO ("YYYY-MM-DD"), EU ("D-M-YYYY"), or relative ("today", "yesterday", "Nd ago", "Nw ago") dates, until is exclusive and defaults to now. The returned frame has time (ISO 8601 UTC), open, high, low, close, volume columns.

fugazi.fetch(provider=..., symbol=..., ...) is the provider-generic form of the same call — handy when the provider name is itself a variable:

df = ta.fetch(provider="yfinance", symbol="AAPL", freq="1d", since="2020-01-01")