finind 0.2.1

A lightweight, production-ready Python library for computing core financial technical indicators such as SMA, EMA, and RSI using numerically stable and vectorized pandas operations. Designed for quantitative research, algorithmic trading, and machine learning pipelines.

finind 📊

finind 📊

A lightweight, fast, and clean financial indicators & signal generation library built on top of pandas.

Designed for:

• Quant research
• Trading systems
• Streamlit dashboards
• Backtesting pipelines

📜 Changelog

See full version history in CHANGELOG.md

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🚀 Features

📈 Indicators

• SMA (Simple Moving Average)
• EMA (Exponential Moving Average)
• RSI (Wilder’s method)
• ATR (Average True Range)

🔁 Crossover Signals

• Golden Cross (e.g., 50/200)
• Death Cross
• Generic crossover detection

📊 Price Structure Signals

• Higher Highs (HH)
• Lower Lows (LL)
• Swing High / Swing Low detection

⚡ Momentum Signals

• RSI Overbought / Oversold
• MACD Bullish / Bearish Crossovers

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🆕 What's New (v0.2.1)

• Added ATR indicator
• Added Higher High / Lower Low detection
• Added Swing High / Swing Low logic
• Added MACD crossover signals
• Improved crossover alignment logic
• Cleaner boolean signal outputs

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📦 Installation

pip install finind

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🧠 Quick Example

import pandas as pd
import yfinance as yf

from finind import sma, ema, rsi, atr
from finind.signals import (
    golden_cross, higher_highs, lower_lows,
    rsi_signal, macd_crossover
)

# Fetch data
df = yf.download("^NSEI", start="2022-01-01")

# Indicators
df["SMA20"] = sma(df, 20)
df["EMA20"] = ema(df, 20)
df["RSI14"] = rsi(df, 14)
df["ATR14"] = atr(df["High"], df["Low"], df["Close"])

# Signals
df["GoldenCross"] = golden_cross(df, 50, 200)
df["HigherHigh"] = higher_highs(df["Close"])
df["LowerLow"] = lower_lows(df["Close"])

rsi_flags = rsi_signal(df["RSI"])

# Example MACD (user computes MACD first)
df["EMA12"] = ema(df, 12)
df["EMA26"] = ema(df, 26)
df["MACD"] = df["EMA12"] - df["EMA26"]
df["Signal"] = ema(df["MACD"], 9)

macd_flags = macd_crossover(df["MACD"], df["Signal"])

df = pd.concat([df, rsi_flags, macd_flags], axis=1)

print(df.tail())

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🔍 Indicator Details

📊 SMA

sma(df, 20)

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📊 EMA

ema(df, 20)

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📊 RSI (Wilder)

rsi(df, 14)
df["RSI14"] = rsi(df, 14)
df["RSI25"] = rsi(df, 25)

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📊 ATR

atr(df["High"], df["Low"], df["Close"], period=14)
df["ATR14"] = atr(df["High"], df["Low"], df["Close"], 14)

Measures volatility using True Range with Wilder smoothing.

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🔁 Signal Details

🔁 Golden Cross

golden_cross(df, 50, 200)

✔ Short-term SMA crosses above long-term SMA

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📉 Death Cross

death_cross(df, 50, 200)

✔ Short-term SMA crosses below long-term SMA

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📈 Higher Highs

higher_highs(df["Close"], window=3)

✔ Detects trend continuation using swing highs

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📉 Lower Lows

lower_lows(df["Close"], window=3)

✔ Detects downtrend continuation using swing lows

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🔥 RSI Signals

rsi_signal(df["RSI"], overbought=70, oversold=30)

Returns:

• rsi_overbought
• rsi_oversold

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⚡ MACD Crossovers

from finind import ema
from finind.signals import macd_crossover

# Step 1: MACD
df["EMA12"] = ema(df, 12)
df["EMA26"] = ema(df, 26)
df["MACD"] = df["EMA12"] - df["EMA26"]

# Step 2: Signal line
df["Signal"] = ema(df["MACD"], 9)

# Step 3: Crossovers
macd_signals = macd_crossover(df["MACD"], df["Signal"])

df = pd.concat([df, macd_signals], axis=1)

Returns:

• macd_bullish
• macd_bearish

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🧩 Design Philosophy

• ✅ Works directly with pandas Series / DataFrames
• ✅ Minimal dependencies
• ✅ Clean boolean outputs for easy backtesting
• ✅ No black-box logic
• ✅ Fully vectorized (fast)

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📜 Version History

v0.2.0

• Added ATR indicator
• Added Higher High / Lower Low detection
• Added MACD crossover signals
• Improved crossover logic

v0.1.0

• Initial release
• SMA, EMA, RSI
• Golden / Death cross

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⚠️ Disclaimer

This library is for educational and research purposes only. Not financial advice.

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🔥 Roadmap

• MACD (full indicator)
• Bollinger Bands
• ADX / Trend strength indicators
• Volume indicators (OBV, VWAP)
• Multi-timeframe support

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⭐ Support

If you find this project useful:

• ⭐ Star the repo on GitHub
• 🐛 Report issues
• 🤝 Contribute improvements

It helps the project grow and reach more developers 🚀