oryon 0.2.9

Production-grade feature and target engineering for quantitative research.

Production-grade feature and forward target engineering for quantitative research.
Rust core. Python API. Streaming and batch, same object.

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The problem

Most feature engineering libraries take a full DataFrame and return a DataFrame. That works in research. In live trading, it forces you to keep a growing history in memory and recompute every feature on every new bar. This doesn't scale and isn't how production systems work.

A second, quieter problem: research code and live code diverge. Any inconsistency between them is a bug waiting to surface in production.

Oryon solves both. Every feature is a stateful object with a fixed memory footprint. You feed it one bar at a time in live trading, or pass the full dataset in research. Same object, same Rust code, same output.

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Install

pip install oryon

No Rust toolchain required. Pre-built wheels for Linux, macOS, and Windows.

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Quick start

Live trading, one bar at a time:

from oryon.features import Ema, ParkinsonVolatility
from oryon import FeaturePipeline

fp = FeaturePipeline(
    features=[
        Ema(["close"], window=20, outputs=["ema_20"]),
        ParkinsonVolatility(["high", "low"], window=20, outputs=["pvol_20"]),
    ],
    input_columns=["close", "high", "low"],
)

# on each new bar from your data feed
result = fp.update([bar.close, bar.high, bar.low])
# [nan, nan]     during warm-up
# [102.4, 0.018] once ready

Research, full dataset at once:

The same feature pipeline (fp) defined above builds your training dataset. See the full quickstart for details.

import pandas as pd
from oryon import TargetPipeline
from oryon.adapters import run_features_pipeline_pandas, run_targets_pipeline_pandas
from oryon.targets import FutureReturn

# fp is the pipeline from the live trading example above
X = run_features_pipeline_pandas(fp, df)
y = run_targets_pipeline_pandas(
    TargetPipeline(
        targets=[FutureReturn(inputs=["close"], horizon=5, outputs=["ret_5"])],
        input_columns=["close"],
    ),
    df,
)
dataset = pd.concat([X, y], axis=1).dropna()
#            ema_20    pvol_20    ret_5
# 2024-01-01    NaN        NaN      NaN   <- warm-up
# ...
# 2024-01-21  102.4    0.01823   0.0312
# 2024-01-22  102.7    0.01754   0.0187
# ...
# 2024-12-27  118.2    0.02341      NaN   <- forward period

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Benchmarks

Rust core, Apple M-series. Python adds a constant ~150 ns per call on top.

Features: per update() call

Feature	w=20	w=200
Ema, SimpleReturn, LogReturn	< 10 ns	< 10 ns
Sma, ParkinsonVolatility, RogersSatchellVolatility	< 30 ns	< 175 ns
Skewness, Kurtosis, LinearSlope	< 40 ns	< 510 ns
Kama	164 ns	870 ns

The goal is every feature under 1 µs at w=200.

Targets: per run_research() call over 1 000 bars

Target	h=20	h=200
FutureReturn	1.9 µs	1.7 µs
FutureCTCVolatility	28 µs	280 µs
FutureLinearSlope	31 µs	287 µs

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Why not build it yourself?

Custom pipelines accumulate silent bugs:

• Look-ahead bias. A feature that accidentally reads future data produces results impossible to replicate in live trading. It will never raise an error.
• State leakage between folds. In cross-validation, state from one fold contaminates the next if not explicitly reset. The numbers look plausible. The model is wrong.
• Research / live divergence. Batch and streaming implementations drift over time. A subtle difference in edge case handling is enough to break a live strategy.

Every feature and target in Oryon ships with contract tests that enforce warm_up_period, forward_period, None propagation, reset correctness, and instance independence. The test infrastructure is part of the public API. Contributions must pass the same contracts.

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Documentation

Full API reference, guides, and benchmarks at oryonlib.dev.

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Contributing

Contributions of features and targets are welcome. See the contributing guide for the full workflow and checklist.

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License

MIT. See LICENSE.

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Developed by Lucas Inglese