Biblioteca de features quantitativas para algorithmic trading (OHLCV -> features).
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Quantmaster
Biblioteca de features quantitativas para adicionar colunas em pandas.DataFrame com dados OHLCV.
Instalação (desenvolvimento)
pip install -e ".[dev]"
Uso rápido
from quantmaster.features.momentum import rsi
from quantmaster.features.volatility import har_rv
df["rsi_10"] = rsi(df, window=10)
df = df.join(har_rv(df))
Estrutura
- Features ficam em
src/quantmaster/features/separadas por categoria (Momentum, Trend, Volatility, etc.). - Cada feature é uma função que recebe
DataFrame(ouSeries) e retornaSeries(ouDataFrame) alinhado ao índice.
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