tradelab 0.1.0

A simple and powerful Python package for algorithmic trading and technical analysis. Calculate technical indicators and analyze market data with ease.

TradeLab

A simple and powerful Python package for algorithmic trading and technical analysis. Calculate technical indicators and analyze market data with ease.

What Does It Do?

TradeLab helps you:

• Calculate technical indicators (moving averages, RSI, SuperTrend, etc.)
• Compare different stocks or assets
• Analyze market data without complex coding

Quick Start

Installation

pip install tradelab

Basic Usage

import pandas as pd

from tradelab.indicators import rsi, ema, supertrend


# Load your stock data (CSV file with columns: date, open, high, low, close, volume)

data = pd.read_csv('your_stock_data.csv')


# Calculate indicators

rsi_values =rsi(data,period=14)

moving_average =ema(data,period=20)

trend_data =supertrend(data,period=10,multiplier=3.0)


print(f"Current RSI: {rsi_values.iloc[-1]:.2f}")

print(f"Current EMA: {moving_average.iloc[-1]:.2f}")

Available Indicators

Trend Indicators

-EMA - Exponential Moving Average

-SuperTrend - Trend following indicator

-Normalized T3 - Smoothed trend oscillator

Momentum Indicators

-RSI - Relative Strength Index (0-100 scale)

-ADX - Average Directional Index

Volatility Indicators

-ATR - Average True Range

Comparative Indicators

-Relative Strength - Compare two stocks or assets

Examples

Calculate RSI values

from tradelab.indicators import rsi


# Calculate RSI

rsi_values =rsi(data,period=14)

print(f"Current RSI: {rsi_values.iloc[-1]:.2f}")


# Check if overbought (>70) or oversold (<30)

if rsi_values.iloc[-1]>70:

    print("Potentially overbought")

elif rsi_values.iloc[-1]<30:

    print("Potentially oversold")

Compare two stocks

from tradelab.indicators import relative_strength


# Compare Apple vs S&P 500

apple_data = pd.read_csv('AAPL.csv')

spy_data = pd.read_csv('SPY.csv')


comparison =relative_strength(apple_data, spy_data)

print(f"Apple vs S&P 500 strength: {comparison['relative_strength'].iloc[-1]:.4f}")

Calculate multiple indicators

from tradelab.indicators import rsi, ema, atr


# Calculate multiple indicators at once

rsi_14 =rsi(data,period=14)

ema_20 =ema(data,period=20)

volatility =atr(data,period=14)


print(f"RSI(14): {rsi_14.iloc[-1]:.2f}")

print(f"EMA(20): {ema_20.iloc[-1]:.2f}")

print(f"ATR(14): {volatility.iloc[-1]:.2f}")

Data Format

Your data should be a pandas DataFrame with these columns:

-date (optional, can be index)

-open - Opening price

-high - Highest price

-low - Lowest price

-close - Closing price

-volume - Trading volume

Example:

date,open,high,low,close,volume

2024-01-01,150.00,152.50,149.00,151.25,1000000

2024-01-02,151.30,153.00,150.50,152.75,1200000

Import Options

You can import indicators in different ways:

# Import everything

from tradelab.indicators import*


# Import specific indicators

from tradelab.indicators import rsi, ema


# Import by category

from tradelab.indicators.momentum import rsi

from tradelab.indicators.trend import ema

Requirements

• Python 3.8+
• pandas
• numpy
• ta-lib
• fixedta

Contributing

We welcome contributions! Feel free to:

• Report bugs
• Suggest new indicators
• Submit pull requests
• Improve documentation

License

MIT License - feel free to use in your projects.

Support

If you find this package helpful, please give it a star ⭐ on GitHub!

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Disclaimer: This package is for educational and research purposes. Always do your own research before making investment decisions.