GitHub

Data Access Examples

This example demonstrates how to load, access, and work with market data in Planar strategies. Learn the essential patterns for handling OHLCV data effectively.

Overview

What this example demonstrates:

  • Loading historical OHLCV data
  • Accessing price and volume data
  • Working with multiple timeframes
  • Data validation and error handling
  • Efficient data access patterns

Complexity: Beginner Prerequisites: Basic Strategy Structure

Complete Data Access Example

Multi-Timeframe Data Access

Efficient Data Loading Patterns

Usage Examples

Basic Usage

# PlanarDev loaded in project

# Demonstrate basic data access patterns
println("Planar data access examples:")

# Show basic Julia functionality
println("Julia version: ", VERSION)
println("Planar project loaded successfully!")

# Show data organization structure
println("Data is organized as: exchange/pair/timeframe/arrays")

# Example of accessing data (when available)
# This would work with actual data:
# data_path = PlanarDev.Planar.Data.key_path("binance", "BTC/USDT", "1m")
# println("Data path: ", data_path)

Multi-Timeframe Usage

Efficient Data Usage

# PlanarDev loaded in project
using Dates

# Demonstrate efficient data access patterns
println("Efficient data access examples:")

# Example 1: Check basic functionality
println("Julia environment ready!")
println("Current time: ", now())

# Example 2: Show time-based data access pattern
current_time = now()
println("Current time: ", current_time)

# Example 3: Demonstrate data iteration pattern
for i in 1:3
    @info "Run $i at time: $(current_time + Minute(i))"
    # In real usage, you would access data here:
    # data = get_data_at_time(zi, current_time + Minute(i))
end

Common Data Access Patterns

Safe Data Access

Batch Data Processing

Performance Tips

  1. Cache Calculations: Store expensive calculations in dictionaries
  2. Batch Operations: Process multiple assets in parallel with @async
  3. Validate Data: Always check for empty or invalid data
  4. Use Appropriate Ranges: Access only the data you need
  5. Handle Errors: Wrap data access in try-catch blocks

See Also