pine-script-py 0.1.1

Pine Script-like indicators and helpers built on pandas and numpy.

pinepy

A Pine Script-like helper library for Python using only pandas and numpy.

Goals

• Provide Pine-style series operations and indicator math in Python.
• Stay lightweight: no dependencies beyond pandas and numpy.
• Keep behavior close to Pine where it matters for calculations.

Non-goals (for now)

• Chart rendering and UI (plotting is not implemented).
• Strategy execution engine (orders, fills, slippage).
• Real-time brokerage integration.

Status

This is a compatibility-focused subset of Pine Script v5. It prioritizes indicator calculations and series behaviors. Plotting, strategy orders, alerts, and UI inputs are stubs.

Install (editable, local dev)

pip install -e .

Install (build + install)

pip install .

Quick usage

import pandas as pd
import pinepy as pine

# Example OHLCV DataFrame
# df = pd.read_csv(...)
close = df["close"]
volume = df["volume"]

ema_20 = pine.ta.ema(close, 20)
rsi_14 = pine.ta.rsi(close, 14)

# Pine-like history reference
ema_20_prev = pine.series.ref(ema_20, 1)

Concepts

Series and history referencing

Pine uses time-series values that can be indexed like close[1] to access previous bars. In pinepy, you can do this via:

from pinepy import Series
from pinepy import series as s

close = Series(df["close"])
prev_close = close[1]         # shift by 1
prev_close_alt = s.ref(close, 1)

NA behavior

• pinepy.math.na is numpy.nan.
• pinepy.math.nz(x, replacement) replaces NA values.

Input stubs

Pine input.* functions are UI-driven. In pinepy, they are simple stubs that return the default value you provide.

API Overview

pinepy.series

• Series: wrapper class with Pine-like history indexing.
• ref(source, length=1): shift series by length.
• nz(source, replacement=0): replace NA values.

pinepy.math

• na: NA constant (numpy.nan).
• isna(source)
• nz(source, replacement=0)
• abs(source)
• sqrt(source)
• pow(source, power)
• max(source, other)
• min(source, other)
• avg(a, b)
• ceil(source)
• floor(source)
• round_(source, precision=0)

pinepy.ta

Core indicators and helpers:

• Moving averages: sma, ema, rma, wma, vwma
• Volatility: stdev, variance, atr, true_range
• Extremes: highest, lowest, highestbars, lowestbars
• Momentum: rsi, roc, change
• Aggregation: sum, cum
• Price composites: hl2, hlc3, ohlc4
• Crosses: cross, crossover, crossunder
• Signal helpers: barssince / barslast, valuewhen

pinepy.array

• Array with get, set, push, pop, size, clear
• new_float, new_int, new_bool, new_string

pinepy.input

• int, float, bool, string, source (all return defaults)

Examples

RSI + EMA signal

import pinepy as pine

close = df["close"]

ema_fast = pine.ta.ema(close, 12)
ema_slow = pine.ta.ema(close, 26)

long_signal = pine.ta.crossover(ema_fast, ema_slow)
short_signal = pine.ta.crossunder(ema_fast, ema_slow)

ATR-based stop

import pinepy as pine

high = df["high"]
low = df["low"]
close = df["close"]

atr_14 = pine.ta.atr(high, low, close, 14)
stop = close - 3 * atr_14

Value when

import pinepy as pine

cond = df["close"] > df["open"]
value = pine.ta.valuewhen(cond, df["close"], 0)

Compatibility notes

• Outputs are pandas Series unless you pass pinepy.Series, in which case outputs are wrapped as pinepy.Series.
• NA behavior follows pandas/numpy unless noted.
• input.* functions are stubs.

Roadmap

Planned additions (incrementally, per request):

• Expand ta to cover the full Pine v5 indicator set.
• More Pine math functions and vectorized helpers.
• Session/time helpers.
• Plot/strategy stubs for API compatibility.

Contributing

If you want a specific Pine function, open an issue or request it directly.