Financial Data Pipeline Development by Jose AcostaFinancial Data Pipeline Development by Jose Acosta

Financial Data Pipeline Development

Jose Acosta

Completed work

Data Engineer

PostgreSQL

Pytest

Python

Investment & Asset Management

Financial Data Pipeline 📈

A modular, enterprise-grade ETL pipeline for financial market data. Supports extraction, transformation, validation, and storage from multiple sources (crypto, equities, derivatives) with advanced analytics and database integration.

Monitoring and Quality Assurance

Error Handling

Performance Optimization

Overview

Exodus v2025 is a professional and modular ETL pipeline for financial data (crypto, stocks, derivatives), with advanced validation, splits, storage in TimescaleDB/PostgreSQL, and export to Parquet/CSV/JSON files.

Requirements

Python: >=3.8

Poetry (recommended) or pip for dependency management

PostgreSQL and TimescaleDB for database storage (if using DB features)

pytest for running tests

5-Minute Quick Start

Option 1: Terminal Quick Start (No Code Required)

# 1. Clone and setup
git clone https://github.com/josetraderx/Financial-Data-Pipeline.git
cd Financial-Data-Pipeline
pip install -r requirements.txt

# 2. Download Bitcoin data for the last 7 days (1-hour intervals)
python -m Data_ETL.cli download --symbol BTCUSDT --timeframe 1h --days 7

# 3. View your data
ls data/processed/  # Your CSV files are here!

Option 2: Python Quick Start (3 Lines of Code)

from Data_ETL.pipelines.crypto_pipeline import CryptoPipeline

# Download and process Bitcoin data
pipeline = CryptoPipeline()
data = pipeline.quick_download('BTCUSDT', timeframe='1h', days=7)
print(f"✅ Downloaded {len(data)} Bitcoin price records!")

Expected Output:

✅ Downloaded 168 Bitcoin price records!
   Timestamp         Open     High      Low    Close      Volume
0  2024-08-04 00:00  65420.5  65580.0  65200.0  65350.2  1250.43
1  2024-08-04 01:00  65350.2  65480.1  65180.5  65420.8  980.52
...

Option 3: One-Liner with File Export

# Download data and save to CSV in one line
CryptoPipeline().quick_download('ETHUSDT', '4h', 14).to_csv('ethereum_2weeks.csv')

Architecture

System Flow Diagram

graph TD
    A[Data Sources] --> B[Data Providers]
    B --> C[Data Validation]
    C --> D[Data Processing]
    D --> E[Data Splitting]
    E --> F[Storage Layer]
    F --> G[Export Formats]
    
    B1[Bybit API] --> B
    B2[Binance API] --> B
    B3[Yahoo Finance] --> B
    
    C --> C1[OHLCV Validation]
    C --> C2[Outlier Detection]
    C --> C3[Missing Data Handling]
    
    F --> F1[TimescaleDB]
    F --> F2[PostgreSQL]
    F --> F3[File System]
    
    G --> G1[Parquet]
    G --> G2[CSV]
    G --> G3[JSON]
    
    subgraph "Monitoring & Quality"
        H[Data Quality Metrics]
        I[Error Logging]
        J[Performance Monitoring]
    end
    
    D --> H
    C --> I
    F --> J

Project Structure

Data_ETL/
├── providers/          # Data provider implementations
│   ├── crypto/         # Cryptocurrency data providers
│   ├── equities/       # Stock market data providers
│   ├── options/        # Options data providers
│   └── other_derivatives/  # Other derivative instruments
├── processing/         # Data processing and validation
│   ├── enhanced_metadata_manager.py  # Centralized metadata management
│   ├── data_cleaner.py              # Data validation and cleaning
│   └── data_splitter.py             # Data splitting utilities
├── storage/            # Database storage solutions
│   ├── timeseries_db.py             # TimescaleDB for time series data
│   └── metadata_db.py               # PostgreSQL for metadata
├── pipelines/          # Complete data pipelines
│   ├── crypto_pipeline.py           # Crypto data pipeline
│   ├── config_manager.py            # Configuration management
│   └── example_usage.py             # Usage examples
├── feeds/              # Data feeds
├── raw/                # Raw data and archiving
├── validation/         # Data validation modules
├── tests/              # Test scripts and setup
├── notebooks/          # Jupyter notebooks and demos
├── cli/                # Command line interface
└── README.md           # Project documentation

Key Features

1. Data Providers

Bybit: Cryptocurrency data with real-time and historical OHLCV data

Extensible: Easy to add new providers (equities, options, etc.)

Rate Limiting: Built-in rate limiting and error handling

2. Data Processing

Enhanced Validation: Comprehensive OHLCV data validation

Data Cleaning: Automated missing value imputation and outlier detection

Quality Scoring: Automated data quality assessment

Metadata Management: Centralized metadata storage and retrieval

3. Data Splitting

Chronological Splits: Time-based train/test splits

Random Splits: Randomized data splitting

Date-based Splits: Split by specific dates

Sliding Windows: Generate sliding window datasets for time series

4. Storage Solutions

TimescaleDB: High-performance time series storage

PostgreSQL: Metadata and data lineage storage

File Storage: Parquet, CSV, and JSON export options

Data Lineage: Track data transformations and relationships

5. Pipeline Orchestration

End-to-End Pipelines: Complete data processing workflows

Configuration Management: Flexible configuration system

Error Handling: Comprehensive error handling and logging

Monitoring: Built-in data quality monitoring

Installation & Setup

1. Basic Installation

# Clone the repository
git clone https://github.com/josetraderx/Financial-Data-Pipeline.git
cd Financial-Data-Pipeline

# Using Poetry (Recommended)
poetry install
poetry shell

# Or using pip
pip install -r requirements.txt

2. Database Setup (Optional)

PostgreSQL + TimescaleDB Installation

# Ubuntu/Debian
sudo apt-get install postgresql postgresql-contrib
sudo apt-get install timescaledb-postgresql

# macOS with Homebrew
brew install postgresql timescaledb

# Create database
sudo -u postgres createdb exodus_data
sudo -u postgres psql -d exodus_data -c "CREATE EXTENSION IF NOT EXISTS timescaledb;"

Database User Setup

-- Connect to PostgreSQL as superuser
sudo -u postgres psql

-- Create user and database
CREATE USER exodus_user WITH PASSWORD 'your_password';
CREATE DATABASE exodus_data OWNER exodus_user;
GRANT ALL PRIVILEGES ON DATABASE exodus_data TO exodus_user;

-- Enable TimescaleDB extension
\c exodus_data
CREATE EXTENSION IF NOT EXISTS timescaledb;

Configuration

Environment Variables

# Database Configuration
export DB_HOST=localhost
export DB_PORT=5432
export DB_NAME=exodus_data
export DB_USER=exodus_user
export DB_PASSWORD=your_password

# Bybit API Configuration
export BYBIT_API_KEY=your_api_key
export BYBIT_API_SECRET=your_api_secret
export BYBIT_TESTNET=true

Configuration File Example

{
  "data_dir": "data/processed",
  "db_config": {
    "host": "localhost",
    "port": 5432,
    "database": "exodus_data",
    "user": "exodus_user",
    "password": "your_password"
  },
  "providers": {
    "bybit": {
      "api_key": "your_api_key",
      "api_secret": "your_api_secret",
      "testnet": true
    }
  },
  "validation_config": {
    "handle_missing": "interpolate",
    "outlier_method": "iqr",
    "outlier_threshold": 1.5
  }
}

Data Validation

The EnhancedDataValidator performs comprehensive data validation:

Validation Checks

Required Columns: Ensures OHLCV columns are present

Data Types: Validates numeric types for price and volume

OHLCV Relationships: Validates price relationships (High >= Low, etc.)

Missing Values: Detects and handles missing data

Outliers: Identifies and handles outliers using IQR method

Duplicates: Detects and removes duplicate records

Timestamps: Validates and regularizes timestamp format

Zero Values: Detects zero prices and volumes

Extreme Changes: Identifies unrealistic price movements

Validation Report

{
    "is_valid": true,
    "total_records": 720,
    "valid_records": 718,
    "invalid_records": 2,
    "missing_values": 0,
    "outliers_detected": 5,
    "duplicates_found": 0,
    "quality_score": 0.97,
    "errors": [],
    "warnings": ["Some outliers detected"],
    "validation_details": {
        "column_stats": {...},
        "missing_analysis": {...},
        "outlier_analysis": {...}
    }
}

Data Storage

TimescaleDB Integration

from Data_ETL.storage.timeseries_db import TimeSeriesDB

# Connect to TimescaleDB
db = TimeSeriesDB(db_config)
db.connect()
db.create_hypertable()

# Insert data
db.insert_data(dataframe)

# Query data
data = db.query_data(
    symbol='BTCUSDT',
    start_time=start_date,
    end_time=end_date
)

Metadata Management

from Data_ETL.storage.metadata_db import MetadataDB

# Connect to metadata database
metadata_db = MetadataDB(db_config)
metadata_db.connect()
metadata_db.create_tables()

# Store metadata
dataset_id = metadata_db.insert_dataset_metadata(metadata)

# Store validation report
metadata_db.insert_validation_report(dataset_id, report)

Data Splitting

Chronological Split

from Data_ETL.processing.data_splitter import DataSplitter

splitter = DataSplitter()
train_data, test_data = splitter.train_test_split(
    data, 
    test_size=0.2, 
    method='chronological'
)

Date-based Split

splits = splitter.split_by_date(
    data, 
    split_date=datetime(2024, 1, 1)
)

Sliding Windows

windows = splitter.create_sliding_windows(
    data, 
    window_size=100, 
    step_size=10
)

Monitoring and Quality Assurance

Data Quality Metrics

Completeness: Percentage of non-null values

Accuracy: Validation rule compliance

Consistency: OHLCV relationship consistency

Timeliness: Data freshness and update frequency

Validity: Data type and range validation

Monitoring Dashboard

The pipeline provides data quality metrics that can be integrated with monitoring systems:

# Get quality metrics
metrics = validator.get_quality_metrics(data)

# Store metrics in database
metadata_db.insert_quality_metrics(dataset_id, metrics)

Error Handling

The pipeline includes comprehensive error handling:

Connection Errors: Database and API connection failures

Data Validation Errors: Invalid data detection and reporting

Processing Errors: Transformation and computation errors

Storage Errors: File and database storage failures

Performance Optimization

Database Optimization

Hypertables: TimescaleDB hypertables for time series data

Indexes: Optimized indexes for common queries

Partitioning: Automatic time-based partitioning

Compression: Built-in data compression

Memory Management

Chunked Processing: Process large datasets in chunks

Lazy Loading: Load data only when needed

Memory Monitoring: Track memory usage during processing

Testing

Unit Tests

# Run unit tests
pytest Data_ETL/processing/tests/

# Run specific test module
pytest Data_ETL/processing/tests/test_data_cleaner.py

Integration Tests

# Run integration tests
pytest tests/setup_test_db.py

Examples

Complete Examples

See Data_ETL/pipelines/example_usage.py for complete working examples including:

Basic Pipeline: Simple data download and processing

Advanced Pipeline: Full pipeline with database storage

Metadata Management: Metadata operations and queries

Data Quality Analysis: Quality assessment and reporting

Basic Pipeline Example

from Data_ETL.pipelines.crypto_pipeline import CryptoPipeline
from Data_ETL.pipelines.config_manager import PipelineConfig

# Load configuration
config = PipelineConfig('config/pipeline_config.json')

# Create pipeline
pipeline = CryptoPipeline(config.get())

# Configure pipeline run
pipeline_config = config.create_pipeline_config(
    provider='bybit',
    symbol='BTCUSDT',
    timeframe='1h',
    days_back=30,
    save_files=True,
    store_db=False
)

# Run pipeline
results = pipeline.run_pipeline(pipeline_config)

Advanced Pipeline with Database Storage

# Configure advanced pipeline
pipeline_config = config.create_pipeline_config(
    provider='bybit',
    symbol='ETHUSDT',
    timeframe='4h',
    days_back=30,
    splits={
        'train_test_split': {
            'test_size': 0.2,
            'method': 'chronological'
        }
    },
    store_db=True,
    save_files=True
)

# Run pipeline
results = pipeline.run_pipeline(pipeline_config)

Running Examples

# Run all examples
python Data_ETL/pipelines/example_usage.py

# Set up configuration
python Data_ETL/pipelines/config_manager.py

Extension Points

Adding New Providers

Create provider class in appropriate subdirectory

Implement required methods (download, authenticate, etc.)

Add provider configuration to config system

Update pipeline to support new provider

Custom Validation Rules

Extend EnhancedDataValidator class

Add custom validation methods

Update validation configuration

Storage Backends

Create new storage class implementing storage interface

Add configuration options

Integrate with pipeline storage system

Frequently Asked Questions (FAQ)

General Questions

Q: What is the minimum Python version required? A: Python 3.8 or higher is required. We recommend Python 3.9+ for best performance.

Q: Do I need a database to use this pipeline? A: No, the database is optional. You can use file-based storage (CSV, Parquet, JSON) without any database setup.

Q: Can I use this for live trading? A: This is primarily a data pipeline for research and analysis. While it can provide real-time data, it's not designed for high-frequency trading.

Installation & Setup

Q: I'm getting import errors after installation A: Make sure you're in the correct virtual environment and all dependencies are installed:

pip install -r requirements.txt
# or
poetry install

Q: How do I set up the database? A: Follow the Installation & Setup section. For testing, you can skip the database and use file storage.

Q: What if I don't have TimescaleDB? A: You can use regular PostgreSQL, but you'll miss out on time-series optimizations. File storage is also available.

Configuration

Q: Where do I put my API keys? A: Use environment variables or the configuration file. Never commit API keys to version control:

export BYBIT_API_KEY=your_key_here
export BYBIT_API_SECRET=your_secret_here

Q: How do I configure different timeframes? A: Supported timeframes include: 1m, 5m, 15m, 30m, 1h, 4h, 6h, 12h, 1d, 1w

Q: Can I download data for multiple symbols at once? A: Yes, use the batch processing features:

symbols = ['BTCUSDT', 'ETHUSDT', 'ADAUSDT']
for symbol in symbols:
    pipeline.quick_download(symbol, '1h', 30)

Data Quality & Validation

Q: What happens if my data has missing values? A: The validator can handle missing values through interpolation, forward fill, or removal based on your configuration.

Q: How do I interpret the quality score? A: Quality scores range from 0-1:

0.9-1.0: Excellent quality

0.8-0.9: Good quality

0.7-0.8: Acceptable quality

<0.7: Poor quality, needs attention

Q: Can I add custom validation rules? A: Yes, extend the EnhancedDataValidator class and add your custom methods.

Performance & Scaling

Q: How much data can the pipeline handle? A: Tested with millions of records. Use chunked processing for very large datasets.

Q: The pipeline is running slowly, what can I do? A:

Reduce the date range

Use larger timeframes (e.g., 4h instead of 1m)

Enable database storage for better performance

Check your internet connection and API rate limits

Q: Can I run multiple pipelines in parallel? A: Yes, but be mindful of API rate limits. Use different API keys or implement proper rate limiting.

Troubleshooting Data Issues

Q: I'm getting "Invalid OHLCV relationship" errors A: This indicates data quality issues. Check:

High >= Low

Open, High, Low, Close > 0

Volume >= 0

Q: Some data points are missing A: This is normal for some exchanges during low-activity periods. The validator can interpolate missing values.

Q: Data looks incorrect/unexpected A: Enable debug mode and check the validation report:

results = pipeline.run_pipeline(config, debug=True)
print(results['validation_report'])

API & Rate Limiting

Q: I'm getting rate limit errors A:

Use testnet for development

Implement proper delays between requests

Consider using a higher-tier API plan

Check your API key permissions

Q: Which exchanges are supported? A: Currently supports Bybit with plans for Binance, Coinbase, and others. Check the providers directory for updates.

Q: Can I use this with paper trading APIs? A: Yes, set testnet=true in your configuration for supported exchanges.

Development & Contributing

Q: How do I add support for a new exchange? A:

Create a new provider class in providers/crypto/

Implement the required interface methods

Add configuration options

Write tests

Submit a pull request

Q: How do I report bugs or request features? A: Open an issue on GitHub with detailed information about the problem or feature request.

Q: Is there a development roadmap? A: Check the project's GitHub issues and milestones for planned features and improvements.

Troubleshooting

Common Issues

Database Connection Failed

Check database credentials and connectivity

Ensure PostgreSQL/TimescaleDB is running

Verify database permissions

API Rate Limiting

Check API key permissions

Implement proper rate limiting

Use testnet for development

Data Validation Failures

Review validation configuration

Check data format and structure

Examine validation report for details

Memory Issues

Reduce batch sizes

Use chunked processing

Monitor memory usage

Import Errors

# Ensure proper installation
pip install -r requirements.txt

# Check Python path
export PYTHONPATH="${PYTHONPATH}:/path/to/Financial-Data-Pipeline"

Configuration Issues

# Verify configuration file exists and is valid JSON
python -c "import json; print(json.load(open('config/pipeline_config.json')))"

Debug Mode

# Enable debug logging
import logging
logging.basicConfig(level=logging.DEBUG)

# Run pipeline with debug info
results = pipeline.run_pipeline(config, debug=True)

Getting Help

Check the FAQ section

Review error messages and logs carefully

Search existing GitHub issues

Create a new issue with detailed error information

Contributing

Follow the existing code structure and patterns

Add comprehensive tests for new features

Update documentation for any changes

Ensure backward compatibility

Follow Python best practices and type hints

Development Setup

# Clone and setup development environment
git clone https://github.com/josetraderx/Financial-Data-Pipeline.git
cd Financial-Data-Pipeline

# Install development dependencies
poetry install --dev
poetry shell

# Run tests
pytest

# Run linting
flake8 Data_ETL/
black Data_ETL/

Pull Request Process

Fork the repository

Create a feature branch

Make your changes

Add/update tests

Update documentation

Submit pull request

License

This module is part of the Exodus v2025 project and follows the project's licensing terms (MIT License).

Like this project

Completed work

Posted Oct 7, 2025

Developed a modular ETL pipeline for financial market data.

Likes

Views

Financial Data Pipeline Development

Jose Acosta

Completed work

Data Engineer

PostgreSQL

Pytest

Python

Investment & Asset Management

Financial Data Pipeline 📈

Monitoring and Quality Assurance

Error Handling

Performance Optimization

Overview

Requirements

Python: >=3.8

Poetry (recommended) or pip for dependency management

PostgreSQL and TimescaleDB for database storage (if using DB features)

pytest for running tests

5-Minute Quick Start

Option 1: Terminal Quick Start (No Code Required)

# 1. Clone and setup
git clone https://github.com/josetraderx/Financial-Data-Pipeline.git
cd Financial-Data-Pipeline
pip install -r requirements.txt

# 2. Download Bitcoin data for the last 7 days (1-hour intervals)
python -m Data_ETL.cli download --symbol BTCUSDT --timeframe 1h --days 7

# 3. View your data
ls data/processed/  # Your CSV files are here!

Option 2: Python Quick Start (3 Lines of Code)

from Data_ETL.pipelines.crypto_pipeline import CryptoPipeline

# Download and process Bitcoin data
pipeline = CryptoPipeline()
data = pipeline.quick_download('BTCUSDT', timeframe='1h', days=7)
print(f"✅ Downloaded {len(data)} Bitcoin price records!")

Expected Output:

✅ Downloaded 168 Bitcoin price records!
   Timestamp         Open     High      Low    Close      Volume
0  2024-08-04 00:00  65420.5  65580.0  65200.0  65350.2  1250.43
1  2024-08-04 01:00  65350.2  65480.1  65180.5  65420.8  980.52
...

Option 3: One-Liner with File Export

# Download data and save to CSV in one line
CryptoPipeline().quick_download('ETHUSDT', '4h', 14).to_csv('ethereum_2weeks.csv')

Architecture

System Flow Diagram

graph TD
    A[Data Sources] --> B[Data Providers]
    B --> C[Data Validation]
    C --> D[Data Processing]
    D --> E[Data Splitting]
    E --> F[Storage Layer]
    F --> G[Export Formats]
    
    B1[Bybit API] --> B
    B2[Binance API] --> B
    B3[Yahoo Finance] --> B
    
    C --> C1[OHLCV Validation]
    C --> C2[Outlier Detection]
    C --> C3[Missing Data Handling]
    
    F --> F1[TimescaleDB]
    F --> F2[PostgreSQL]
    F --> F3[File System]
    
    G --> G1[Parquet]
    G --> G2[CSV]
    G --> G3[JSON]
    
    subgraph "Monitoring & Quality"
        H[Data Quality Metrics]
        I[Error Logging]
        J[Performance Monitoring]
    end
    
    D --> H
    C --> I
    F --> J

Project Structure

Data_ETL/
├── providers/          # Data provider implementations
│   ├── crypto/         # Cryptocurrency data providers
│   ├── equities/       # Stock market data providers
│   ├── options/        # Options data providers
│   └── other_derivatives/  # Other derivative instruments
├── processing/         # Data processing and validation
│   ├── enhanced_metadata_manager.py  # Centralized metadata management
│   ├── data_cleaner.py              # Data validation and cleaning
│   └── data_splitter.py             # Data splitting utilities
├── storage/            # Database storage solutions
│   ├── timeseries_db.py             # TimescaleDB for time series data
│   └── metadata_db.py               # PostgreSQL for metadata
├── pipelines/          # Complete data pipelines
│   ├── crypto_pipeline.py           # Crypto data pipeline
│   ├── config_manager.py            # Configuration management
│   └── example_usage.py             # Usage examples
├── feeds/              # Data feeds
├── raw/                # Raw data and archiving
├── validation/         # Data validation modules
├── tests/              # Test scripts and setup
├── notebooks/          # Jupyter notebooks and demos
├── cli/                # Command line interface
└── README.md           # Project documentation

Key Features

1. Data Providers

Bybit: Cryptocurrency data with real-time and historical OHLCV data

Extensible: Easy to add new providers (equities, options, etc.)

Rate Limiting: Built-in rate limiting and error handling

2. Data Processing

Enhanced Validation: Comprehensive OHLCV data validation

Data Cleaning: Automated missing value imputation and outlier detection

Quality Scoring: Automated data quality assessment

Metadata Management: Centralized metadata storage and retrieval

3. Data Splitting

Chronological Splits: Time-based train/test splits

Random Splits: Randomized data splitting

Date-based Splits: Split by specific dates

Sliding Windows: Generate sliding window datasets for time series

4. Storage Solutions

TimescaleDB: High-performance time series storage

PostgreSQL: Metadata and data lineage storage

File Storage: Parquet, CSV, and JSON export options

Data Lineage: Track data transformations and relationships

5. Pipeline Orchestration

End-to-End Pipelines: Complete data processing workflows

Configuration Management: Flexible configuration system

Error Handling: Comprehensive error handling and logging

Monitoring: Built-in data quality monitoring

Installation & Setup

1. Basic Installation

# Clone the repository
git clone https://github.com/josetraderx/Financial-Data-Pipeline.git
cd Financial-Data-Pipeline

# Using Poetry (Recommended)
poetry install
poetry shell

# Or using pip
pip install -r requirements.txt

2. Database Setup (Optional)

PostgreSQL + TimescaleDB Installation

# Ubuntu/Debian
sudo apt-get install postgresql postgresql-contrib
sudo apt-get install timescaledb-postgresql

# macOS with Homebrew
brew install postgresql timescaledb

# Create database
sudo -u postgres createdb exodus_data
sudo -u postgres psql -d exodus_data -c "CREATE EXTENSION IF NOT EXISTS timescaledb;"

Database User Setup

-- Connect to PostgreSQL as superuser
sudo -u postgres psql

-- Create user and database
CREATE USER exodus_user WITH PASSWORD 'your_password';
CREATE DATABASE exodus_data OWNER exodus_user;
GRANT ALL PRIVILEGES ON DATABASE exodus_data TO exodus_user;

-- Enable TimescaleDB extension
\c exodus_data
CREATE EXTENSION IF NOT EXISTS timescaledb;

Configuration

Environment Variables

# Database Configuration
export DB_HOST=localhost
export DB_PORT=5432
export DB_NAME=exodus_data
export DB_USER=exodus_user
export DB_PASSWORD=your_password

# Bybit API Configuration
export BYBIT_API_KEY=your_api_key
export BYBIT_API_SECRET=your_api_secret
export BYBIT_TESTNET=true

Configuration File Example

{
  "data_dir": "data/processed",
  "db_config": {
    "host": "localhost",
    "port": 5432,
    "database": "exodus_data",
    "user": "exodus_user",
    "password": "your_password"
  },
  "providers": {
    "bybit": {
      "api_key": "your_api_key",
      "api_secret": "your_api_secret",
      "testnet": true
    }
  },
  "validation_config": {
    "handle_missing": "interpolate",
    "outlier_method": "iqr",
    "outlier_threshold": 1.5
  }
}

Data Validation

The EnhancedDataValidator performs comprehensive data validation:

Validation Checks

Required Columns: Ensures OHLCV columns are present

Data Types: Validates numeric types for price and volume

OHLCV Relationships: Validates price relationships (High >= Low, etc.)

Missing Values: Detects and handles missing data

Outliers: Identifies and handles outliers using IQR method

Duplicates: Detects and removes duplicate records

Timestamps: Validates and regularizes timestamp format

Zero Values: Detects zero prices and volumes

Extreme Changes: Identifies unrealistic price movements

Validation Report

{
    "is_valid": true,
    "total_records": 720,
    "valid_records": 718,
    "invalid_records": 2,
    "missing_values": 0,
    "outliers_detected": 5,
    "duplicates_found": 0,
    "quality_score": 0.97,
    "errors": [],
    "warnings": ["Some outliers detected"],
    "validation_details": {
        "column_stats": {...},
        "missing_analysis": {...},
        "outlier_analysis": {...}
    }
}

Data Storage

TimescaleDB Integration

from Data_ETL.storage.timeseries_db import TimeSeriesDB

# Connect to TimescaleDB
db = TimeSeriesDB(db_config)
db.connect()
db.create_hypertable()

# Insert data
db.insert_data(dataframe)

# Query data
data = db.query_data(
    symbol='BTCUSDT',
    start_time=start_date,
    end_time=end_date
)

Metadata Management

from Data_ETL.storage.metadata_db import MetadataDB

# Connect to metadata database
metadata_db = MetadataDB(db_config)
metadata_db.connect()
metadata_db.create_tables()

# Store metadata
dataset_id = metadata_db.insert_dataset_metadata(metadata)

# Store validation report
metadata_db.insert_validation_report(dataset_id, report)

Data Splitting

Chronological Split

from Data_ETL.processing.data_splitter import DataSplitter

splitter = DataSplitter()
train_data, test_data = splitter.train_test_split(
    data, 
    test_size=0.2, 
    method='chronological'
)

Date-based Split

splits = splitter.split_by_date(
    data, 
    split_date=datetime(2024, 1, 1)
)

Sliding Windows

windows = splitter.create_sliding_windows(
    data, 
    window_size=100, 
    step_size=10
)

Monitoring and Quality Assurance

Data Quality Metrics

Completeness: Percentage of non-null values

Accuracy: Validation rule compliance

Consistency: OHLCV relationship consistency

Timeliness: Data freshness and update frequency

Validity: Data type and range validation

Monitoring Dashboard

The pipeline provides data quality metrics that can be integrated with monitoring systems:

# Get quality metrics
metrics = validator.get_quality_metrics(data)

# Store metrics in database
metadata_db.insert_quality_metrics(dataset_id, metrics)

Error Handling

The pipeline includes comprehensive error handling:

Connection Errors: Database and API connection failures

Data Validation Errors: Invalid data detection and reporting

Processing Errors: Transformation and computation errors

Storage Errors: File and database storage failures

Performance Optimization

Database Optimization

Hypertables: TimescaleDB hypertables for time series data

Indexes: Optimized indexes for common queries

Partitioning: Automatic time-based partitioning

Compression: Built-in data compression

Memory Management

Chunked Processing: Process large datasets in chunks

Lazy Loading: Load data only when needed

Memory Monitoring: Track memory usage during processing

Testing

Unit Tests

# Run unit tests
pytest Data_ETL/processing/tests/

# Run specific test module
pytest Data_ETL/processing/tests/test_data_cleaner.py

Integration Tests

# Run integration tests
pytest tests/setup_test_db.py

Examples

Complete Examples

See Data_ETL/pipelines/example_usage.py for complete working examples including:

Basic Pipeline: Simple data download and processing

Advanced Pipeline: Full pipeline with database storage

Metadata Management: Metadata operations and queries

Data Quality Analysis: Quality assessment and reporting

Basic Pipeline Example

from Data_ETL.pipelines.crypto_pipeline import CryptoPipeline
from Data_ETL.pipelines.config_manager import PipelineConfig

# Load configuration
config = PipelineConfig('config/pipeline_config.json')

# Create pipeline
pipeline = CryptoPipeline(config.get())

# Configure pipeline run
pipeline_config = config.create_pipeline_config(
    provider='bybit',
    symbol='BTCUSDT',
    timeframe='1h',
    days_back=30,
    save_files=True,
    store_db=False
)

# Run pipeline
results = pipeline.run_pipeline(pipeline_config)

Advanced Pipeline with Database Storage

# Configure advanced pipeline
pipeline_config = config.create_pipeline_config(
    provider='bybit',
    symbol='ETHUSDT',
    timeframe='4h',
    days_back=30,
    splits={
        'train_test_split': {
            'test_size': 0.2,
            'method': 'chronological'
        }
    },
    store_db=True,
    save_files=True
)

# Run pipeline
results = pipeline.run_pipeline(pipeline_config)

Running Examples

# Run all examples
python Data_ETL/pipelines/example_usage.py

# Set up configuration
python Data_ETL/pipelines/config_manager.py

Extension Points

Adding New Providers

Create provider class in appropriate subdirectory

Implement required methods (download, authenticate, etc.)

Add provider configuration to config system

Update pipeline to support new provider

Custom Validation Rules

Extend EnhancedDataValidator class

Add custom validation methods

Update validation configuration

Storage Backends

Create new storage class implementing storage interface

Add configuration options

Integrate with pipeline storage system

Frequently Asked Questions (FAQ)

General Questions

Q: What is the minimum Python version required? A: Python 3.8 or higher is required. We recommend Python 3.9+ for best performance.

Q: Do I need a database to use this pipeline? A: No, the database is optional. You can use file-based storage (CSV, Parquet, JSON) without any database setup.

Q: Can I use this for live trading? A: This is primarily a data pipeline for research and analysis. While it can provide real-time data, it's not designed for high-frequency trading.

Installation & Setup

Q: I'm getting import errors after installation A: Make sure you're in the correct virtual environment and all dependencies are installed:

pip install -r requirements.txt
# or
poetry install

Q: How do I set up the database? A: Follow the Installation & Setup section. For testing, you can skip the database and use file storage.

Q: What if I don't have TimescaleDB? A: You can use regular PostgreSQL, but you'll miss out on time-series optimizations. File storage is also available.

Configuration

Q: Where do I put my API keys? A: Use environment variables or the configuration file. Never commit API keys to version control:

export BYBIT_API_KEY=your_key_here
export BYBIT_API_SECRET=your_secret_here

Q: How do I configure different timeframes? A: Supported timeframes include: 1m, 5m, 15m, 30m, 1h, 4h, 6h, 12h, 1d, 1w

Q: Can I download data for multiple symbols at once? A: Yes, use the batch processing features:

symbols = ['BTCUSDT', 'ETHUSDT', 'ADAUSDT']
for symbol in symbols:
    pipeline.quick_download(symbol, '1h', 30)

Data Quality & Validation

Q: What happens if my data has missing values? A: The validator can handle missing values through interpolation, forward fill, or removal based on your configuration.

Q: How do I interpret the quality score? A: Quality scores range from 0-1:

0.9-1.0: Excellent quality

0.8-0.9: Good quality

0.7-0.8: Acceptable quality

<0.7: Poor quality, needs attention

Q: Can I add custom validation rules? A: Yes, extend the EnhancedDataValidator class and add your custom methods.

Performance & Scaling

Q: How much data can the pipeline handle? A: Tested with millions of records. Use chunked processing for very large datasets.

Q: The pipeline is running slowly, what can I do? A:

Reduce the date range

Use larger timeframes (e.g., 4h instead of 1m)

Enable database storage for better performance

Check your internet connection and API rate limits

Q: Can I run multiple pipelines in parallel? A: Yes, but be mindful of API rate limits. Use different API keys or implement proper rate limiting.

Troubleshooting Data Issues

Q: I'm getting "Invalid OHLCV relationship" errors A: This indicates data quality issues. Check:

High >= Low

Open, High, Low, Close > 0

Volume >= 0

Q: Some data points are missing A: This is normal for some exchanges during low-activity periods. The validator can interpolate missing values.

Q: Data looks incorrect/unexpected A: Enable debug mode and check the validation report:

results = pipeline.run_pipeline(config, debug=True)
print(results['validation_report'])

API & Rate Limiting

Q: I'm getting rate limit errors A:

Use testnet for development

Implement proper delays between requests

Consider using a higher-tier API plan

Check your API key permissions

Q: Which exchanges are supported? A: Currently supports Bybit with plans for Binance, Coinbase, and others. Check the providers directory for updates.

Q: Can I use this with paper trading APIs? A: Yes, set testnet=true in your configuration for supported exchanges.

Development & Contributing

Q: How do I add support for a new exchange? A:

Create a new provider class in providers/crypto/

Implement the required interface methods

Add configuration options

Write tests

Submit a pull request

Q: How do I report bugs or request features? A: Open an issue on GitHub with detailed information about the problem or feature request.

Q: Is there a development roadmap? A: Check the project's GitHub issues and milestones for planned features and improvements.

Troubleshooting

Common Issues

Database Connection Failed

Check database credentials and connectivity

Ensure PostgreSQL/TimescaleDB is running

Verify database permissions

API Rate Limiting

Check API key permissions

Implement proper rate limiting

Use testnet for development

Data Validation Failures

Review validation configuration

Check data format and structure

Examine validation report for details

Memory Issues

Reduce batch sizes

Use chunked processing

Monitor memory usage

Import Errors

# Ensure proper installation
pip install -r requirements.txt

# Check Python path
export PYTHONPATH="${PYTHONPATH}:/path/to/Financial-Data-Pipeline"

Configuration Issues

# Verify configuration file exists and is valid JSON
python -c "import json; print(json.load(open('config/pipeline_config.json')))"

Debug Mode

# Enable debug logging
import logging
logging.basicConfig(level=logging.DEBUG)

# Run pipeline with debug info
results = pipeline.run_pipeline(config, debug=True)

Getting Help

Check the FAQ section

Review error messages and logs carefully

Search existing GitHub issues

Create a new issue with detailed error information

Contributing

Follow the existing code structure and patterns

Add comprehensive tests for new features

Update documentation for any changes

Ensure backward compatibility

Follow Python best practices and type hints

Development Setup

# Clone and setup development environment
git clone https://github.com/josetraderx/Financial-Data-Pipeline.git
cd Financial-Data-Pipeline

# Install development dependencies
poetry install --dev
poetry shell

# Run tests
pytest

# Run linting
flake8 Data_ETL/
black Data_ETL/

Pull Request Process

Fork the repository

Create a feature branch

Make your changes

Add/update tests

Update documentation

Submit pull request

License

This module is part of the Exodus v2025 project and follows the project's licensing terms (MIT License).

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Completed work

Posted Oct 7, 2025

Developed a modular ETL pipeline for financial market data.

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Financial Data Pipeline Development

Financial Data Pipeline 📈

Table of Contents

Overview

Requirements

5-Minute Quick Start

Option 1: Terminal Quick Start (No Code Required)

Option 2: Python Quick Start (3 Lines of Code)

Option 3: One-Liner with File Export

Architecture

System Flow Diagram

Project Structure

Key Features

1. Data Providers

2. Data Processing

3. Data Splitting

4. Storage Solutions

5. Pipeline Orchestration

Installation & Setup

1. Basic Installation

2. Database Setup (Optional)

Configuration

Environment Variables

Configuration File Example

Data Validation

Validation Checks

Validation Report

Data Storage

TimescaleDB Integration

Metadata Management

Data Splitting

Chronological Split

Date-based Split

Sliding Windows

Monitoring and Quality Assurance

Data Quality Metrics

Monitoring Dashboard

Error Handling

Performance Optimization

Database Optimization

Memory Management

Testing

Unit Tests

Integration Tests

Examples

Complete Examples

Basic Pipeline Example

Advanced Pipeline with Database Storage

Running Examples

Extension Points

Adding New Providers

Custom Validation Rules

Storage Backends

Frequently Asked Questions (FAQ)

General Questions

Installation & Setup

Configuration

Data Quality & Validation

Performance & Scaling

Troubleshooting Data Issues

API & Rate Limiting

Development & Contributing

Troubleshooting

Common Issues

Debug Mode

Getting Help

Contributing

Development Setup

Pull Request Process

License

Financial Data Pipeline Development

Financial Data Pipeline 📈

Table of Contents

Overview

Requirements

5-Minute Quick Start

Option 1: Terminal Quick Start (No Code Required)

Option 2: Python Quick Start (3 Lines of Code)

Option 3: One-Liner with File Export

Architecture