Data Management

Learn how to work with market data, upload your own data sources, and manage data for backtesting.

Understanding Data in Lona

Trading strategies need historical market data for backtesting. Lona supports two types of data:

1. Global Data (Pre-loaded)

What It Is: Market data provided by the platform Accessibility: Available to all users Identification: Marked with a 🌐 globe icon Usage: Select during execution, no upload needed

Common Global Data:

Major stock indices (SPY, QQQ, DIA)
Popular cryptocurrencies (BTCUSDT, ETHUSDT)
Forex pairs (EURUSD, GBPUSD)
Commodities (Gold, Oil)

2. Your Data (User-uploaded)

What It Is: Custom data you upload Accessibility: Only visible to you Usage: Select during execution like global data Control: You can edit or delete

Why Upload Your Own Data:

Test on specific symbols not in global library
Use higher-frequency data (minute, tick)
Include custom indicators or signals
Incorporate alternative data (sentiment, news)

Data Types

Lona supports two main data formats:

OHLCV Data (Market Prices)

What It Is: Standard market price data

Required Columns:

Timestamp: Date/time of the bar
Open: Opening price
High: Highest price
Low: Lowest price
Close: Closing price
Volume: Trading volume (optional)

Use Cases:

Standard price-based strategies
Technical indicator calculations
Traditional trading algorithms

Custom Data (Non-Price Data)

What It Is: Any time-series data beyond prices

Examples:

Sentiment scores
News event counts
Economic indicators
Social media metrics
Custom computed values

Use Cases:

Alternative data strategies
Fundamental analysis
Sentiment-based trading
Multi-factor models

Note: Custom data is for advanced users creating strategies that incorporate non-traditional data sources.

Uploading Data

Accessing the Upload Dialog

Click Data in the left sidebar
Click the Upload button (+ icon)
Upload dialog opens

Step 1: Select Your File

Supported Format: CSV (Comma-Separated Values)

How to Upload:

Drag and drop a CSV file into the upload area, or
Click to browse and select a file

File Requirements:

Must be a valid CSV file
Should have consistent column structure
Recommended: Include headers in first row
Maximum size: Varies by plan (typically 50MB)

Step 2: Configure File Settings

After selecting a file, configure these settings:

Headers Checkbox

“CSV file has headers in first row”

✅ Checked: First row contains column names
☐ Unchecked: No headers, columns will be named automatically

Example with headers:


timestamp,open,high,low,close,volume
2023-01-01,100.5,101.2,100.1,100.8,1000000

Example without headers:


2023-01-01,100.5,101.2,100.1,100.8,1000000

Tip: Always include headers for clarity. Lona will auto-generate them if missing.

Data Type Selection

“Custom data (News, Sentiment, or other non-OHLCV data)”

☐ Unchecked: Standard OHLCV market data (default)
✅ Checked: Custom alternative data

Choose Based on Your File:

Price data (OHLC) → Leave unchecked
Sentiment scores → Check the box
News counts → Check the box
Economic indicators → Check the box

Step 3: OHLCV Data Configuration

If uploading standard market data (OHLCV), you’ll configure:

Symbol Name

Give your data source a meaningful name:

Good Names:

“AAPL Daily 2010-2023”
“BTCUSDT 1h Binance”
“SPY 5min”

Include:

Symbol/ticker
Timeframe (daily, hourly, 5min)
Source (optional)
Date range (optional)

Column Mapping

Map your CSV columns to required data fields:

Required Mappings:

Timestamp: Date/time column
Close: Closing price column

Optional Mappings:

Open: Opening price
High: Highest price
Low: Lowest price
Volume: Trading volume

How to Map:

For each field, select the corresponding column from dropdown
If column doesn’t exist in your file, select “none”
At minimum, map Timestamp and Close

Example Mapping:

Your CSV columns: date, open_price, high_price, low_price, close_price, vol

Map as:

Timestamp → date
Open → open_price
High → high_price
Low → low_price
Close → close_price
Volume → vol

Data Preview

You’ll see a preview showing:

First 5-10 rows of your data
Column headers
Sample values

Check the Preview: ✅ Dates look correct ✅ Prices are in right columns ✅ Values are properly formatted ✅ No obvious errors

Frequency

Select the data frequency from dropdown:

Daily: One bar per day (most common)
Hourly: One bar per hour
Minute: One bar per minute
Other: Custom frequency

Choose the frequency that matches your data.

Timezone

Select the timezone of your timestamp data:

Common options:

UTC: Universal Coordinated Time (recommended for crypto)
America/New_York: Eastern Time (US stocks)
Europe/London: UK Time (LSE stocks)
Asia/Tokyo: Japan Time (TSE stocks)

Important: Select the timezone your data was recorded in, not your local timezone.

Exchange (Optional)

Name the exchange where data originated:

NYSE
NASDAQ
Binance
Coinbase
Etc.

This is informational and helps organize your data.

Step 4: Custom Data Configuration

If you checked “Custom data”, you’ll configure differently:

Symbol Name

Name describing your custom data:

Examples:

“Twitter Sentiment BTC Daily”
“WSB Mentions Count”
“Economic Calendar Events”
“News Sentiment SPY”

Column Mapping

Required:

Timestamp: Date/time column (required)
Value Columns: Map all columns you want to use

How It Works:

All non-timestamp columns become available to your strategy
Strategy can reference them by column name
No predefined structure required

Example Custom Data:

CSV:


date,sentiment_score,mention_count,positive_ratio
2023-01-01,0.65,1234,0.72
2023-01-02,0.58,987,0.68

Map:

Timestamp → date
Custom columns → sentiment_score, mention_count, positive_ratio

Step 5: Review and Upload

Review all settings in the dialog
Check the preview one more time
Click Upload button

Processing:

File uploads to server
Data is validated
Columns are parsed
Indexes are created
Available for use in ~5-30 seconds

Success: You’ll see a success message and the dialog closes

Error: If upload fails, you’ll see specific error message (see Troubleshooting section)

Data Formats and Requirements

CSV File Format

Structure:


timestamp,open,high,low,close,volume
2023-01-01 09:30:00,100.50,101.20,100.10,100.80,1000000
2023-01-01 10:30:00,100.80,101.50,100.60,101.20,1200000

Requirements:

✅ Comma-separated values
✅ One row per time period
✅ Consistent number of columns
✅ Valid timestamps
✅ Numeric price/volume values

Common Issues:

❌ Using semicolons instead of commas
❌ Missing values (empty cells)
❌ Inconsistent date formats
❌ Non-numeric values in price columns

Timestamp Formats

Supported Formats:

Date Only:


2023-01-01
01/01/2023
2023-01-01
Jan 1, 2023

Date and Time:


2023-01-01 09:30:00
2023-01-01T09:30:00
2023-01-01 09:30:00.000
01/01/2023 09:30 AM

With Timezone:


2023-01-01 09:30:00 UTC
2023-01-01 09:30:00-05:00
2023-01-01T09:30:00Z

Best Practice: Use ISO 8601 format: YYYY-MM-DD HH:MM:SS

Price Data Format

Valid Price Values:


100.50    ✅ Decimal
100       ✅ Integer
1.5e2     ✅ Scientific notation

Invalid Price Values:


$100.50   ❌ Currency symbol
100,50    ❌ Comma as decimal separator
N/A       ❌ Non-numeric

Volume Format:

Whole numbers: 1000000
Can include decimals: 1000000.5
No commas: Use 1000000 not 1,000,000

Managing Your Data

Viewing Data Sources

Navigate to Data section
See all available data sources:
- Global data (🌐 icon)
- Your uploaded data

Data Information

For each data source, you can view:

Name: Symbol or data source name
Type: OHLCV or Custom
Frequency: Daily, hourly, etc.
Date Range: Available time period
Data Points: Number of records
Source: Global or User-uploaded

Editing Data

For Your Uploaded Data:

Click the Edit icon (pencil)
Modify metadata:
- Change name
- Update description
- Adjust frequency
- Change timezone
Save changes

Note: You cannot edit the actual data values, only metadata. To change data, delete and re-upload.

Deleting Data

Find your uploaded data source
Click Delete icon (trash)
Confirm deletion
⚠️ Warning: This cannot be undone

Before Deleting:

Verify you have backup of the file
Check if any strategies depend on this data
Consider if you might need it later

Data Best Practices

File Preparation

✅ Clean Your Data:

Remove duplicate timestamps
Fill missing values
Verify price accuracy
Check for outliers

✅ Consistent Format:

Use same date format throughout
Consistent decimal places
Same column order

✅ Include Headers:

Always add column names
Use clear, descriptive names
Avoid special characters

Naming Conventions

Use descriptive, consistent names:

Good Names:


"SPY-Daily-2010-2023"
"BTCUSDT-1h-Binance"
"AAPL-5min-2023"
"Twitter-Sentiment-Daily"

Poor Names:


"data"
"test"
"file1"
"download"

Data Quality

Before Uploading:

✅ Verify data source reliability
✅ Check for gaps in timestamps
✅ Ensure price data is adjusted for splits/dividends
✅ Validate volume data
✅ Test with small sample first

After Uploading:

✅ Run a quick backtest to verify data works
✅ Check results make sense
✅ Verify timestamp alignment

Common Data Sources

Where to Get Data

Free Sources:

Yahoo Finance: Historical stock/ETF data
Alpha Vantage: API for stocks, forex, crypto
Binance: Crypto historical data
Quandl: Economic and financial data

Paid Sources:

Bloomberg: Professional market data
Refinitiv: Financial market data
Polygon.io: Real-time and historical data
IEX Cloud: US stock market data

Exporting Data

Most sources let you download CSV files:

Select symbol and timeframe
Choose date range
Download as CSV
Upload to Lona

Tip: Save original files as backups before uploading.

Advanced Topics

Multi-Symbol Datasets

To test strategies across multiple symbols:

Option 1: Separate Uploads

Upload each symbol individually
Select multiple during execution
Each symbol is a separate data source

Option 2: Combined File (Advanced)

Include symbol identifier column
More complex upload process
Better for portfolio strategies

High-Frequency Data

For minute or tick data:

Considerations:

Larger file sizes
Longer upload times
Longer backtest times
More detailed results

Recommendations:

Start with daily data
Move to intraday after validating logic
Use smaller date ranges for testing

Data Alignment

When using multiple data sources:

Lona Automatically:

Aligns timestamps across sources
Handles missing data points
Synchronizes different frequencies

You Should:

Use same timezone for all sources
Ensure overlapping date ranges
Verify data quality

Troubleshooting

”Upload Failed” Error

Causes & Solutions:

Invalid CSV Format:

Check file is saved as CSV (not XLSX)
Verify comma-separated (not semicolon or tab)
Ensure UTF-8 encoding

File Too Large:

Reduce date range
Remove unnecessary columns
Split into multiple files

Invalid Data:

Check for non-numeric values in price columns
Verify timestamps are valid dates
Remove empty rows

”Invalid Column Mapping”

Problem: Required columns not mapped correctly

Solution:

Ensure Timestamp and Close are mapped
Verify column names match your file
Check dropdown selections

”Duplicate Symbol Name”

Problem: Name already exists

Solution:

Choose a different name
Add distinguishing detail (date range, source)
Delete old data source if replacing it

”Date Parsing Error”

Problem: Timestamps not recognized

Solution:

Verify date format consistency
Use standard format (YYYY-MM-DD)
Check for invalid dates (Feb 30, etc.)
Ensure timezone is correct

Data Upload Hangs

Problem: Upload process doesn’t complete

Solutions:

Check internet connection
Try smaller file
Refresh and try again
Clear browser cache

Quick Reference

OHLCV Data Checklist

Custom Data Checklist

What’s Next?

Now that you understand data management:

Canvas & Interface: Master the Lona UI
Best Practices: Optimize your workflow
Troubleshooting: Solve common issues

Next: Learn about the Canvas & Interface to navigate Lona efficiently.