Examples

This section provides practical examples demonstrating how to use Rhoa for various technical analysis and machine learning tasks.

Example Categories

• Basic Indicators
  • Simple Moving Average (SMA)
  • Exponential Moving Average (EWMA)
  • Relative Strength Index (RSI)
  • Moving Variance and Standard Deviation
  • Williams %R
  • Combining Multiple Indicators
  • Next Steps
• Advanced Indicators
  • Using the DataFrame Accessor
  • MACD (Moving Average Convergence Divergence)
  • Bollinger Bands
  • ADX (Average Directional Index)
  • Average True Range (ATR)
  • Stochastic Oscillator
  • CCI (Commodity Channel Index)
  • Parabolic SAR
  • Multi-Indicator Strategies
  • Next Steps
• Target Generation
  • Overview
  • Auto Mode (Pareto Optimization)
  • Manual Mode (Elbow Method)
  • Choosing the Right Target
  • Validating Targets
  • Practical Examples
  • Saving and Loading Metadata
  • Best Practices
  • Next Steps
• Complete ML Pipeline
  • Basic Pipeline
  • Advanced Pipeline
  • Multiple Model Comparison
  • Production Pipeline
  • Complete Example
  • Next Steps

Overview

The examples are organized by complexity and use case:

Basic Indicators

Simple examples using individual technical indicators like SMA, RSI, and moving averages. Perfect for beginners.

Advanced Indicators

Examples using complex indicators that require OHLC data: MACD, Bollinger Bands, ADX, Stochastic Oscillator, and more.

Target Generation

Learn how to generate optimized binary targets for machine learning models using both auto and manual modes.

Complete Pipeline

End-to-end examples showing how to build complete ML pipelines from data loading to model training and evaluation.

Quick Reference

Common Tasks

Task	Example Link
Calculate Simple Moving Average	Simple Moving Average (SMA)
Find Overbought Conditions (RSI)	Relative Strength Index (RSI)
Detect MACD Crossovers	MACD (Moving Average Convergence Divergence)
Calculate Bollinger Bands	Bollinger Bands
Generate ML Targets (Auto Mode)	Auto Mode (Pareto Optimization)
Generate ML Targets (Manual Mode)	Manual Mode (Elbow Method)
Build Complete ML Pipeline	Basic Pipeline

Example Data

Most examples assume you have OHLC (Open, High, Low, Close) price data in a CSV file:

import pandas as pd
import rhoa

# Load your data
df = pd.read_csv('your_prices.csv')

# Expected columns: Date, Open, High, Low, Close, Volume
# Date should be parseable as datetime

Sample Data Format

Your CSV file should look like this:

Date,Open,High,Low,Close,Volume
2024-01-01,100.0,105.0,99.0,103.0,1000000
2024-01-02,103.0,107.0,102.0,106.0,1200000
2024-01-03,106.0,108.0,104.0,105.0,950000
...

Getting Test Data

For testing, you can use the sample data included in the tests directory:

import pandas as pd

# Using Rhoa's test data
df = pd.read_csv('https://raw.githubusercontent.com/nainajnahO/Rhoa/main/tests/data.csv')

Or download financial data using yfinance:

import yfinance as yf

# Download stock data
ticker = yf.Ticker("AAPL")
df = ticker.history(period="1y")
df = df.reset_index()

Next Steps

Start with Basic Indicators if you’re new to Rhoa, then progress to more advanced examples as you become comfortable with the basics.