In financial analysis, innovation often comes from unique perspectives on data. Analysts, traders, and researchers are always searching for new ways to understand markets—whether through traditional financial metrics, macroeconomic indicators, or alternative data sources.
However, developing novel financial models remains difficult:
- Fragmented Data – Critical datasets exist across multiple platforms, often behind paywalls.
- Complex Integrations – Analysts must spend valuable time merging and cleaning disparate data sources.
- Limited Experimentation – Many platforms lack the flexibility to explore unconventional correlations.
At Baselight, we make it easy to discover, combine, and analyze structured datasets—giving financial analysts on-demand access to alternative and traditional data sources in a unified marketplace.
How Baselight helps
Baselight eliminates the inefficiencies of fragmented financial analysis by offering:
- Unified Data Access – Access structured datasets across financial markets, commodities, macroeconomic indicators, and alternative data sources.
- Integrated Querying – Seamlessly merge traditional and unconventional datasets without complex ETL processes.
- Advanced Analytics – Build, test, and iterate financial models with instant feedback and visualization tools.
- Flexible Monetization – Share or sell your insights by listing datasets, reports, and models on Baselight’s decentralized marketplace.
With Baselight, financial professionals gain the ability to go beyond standard financial models and create data-driven insights that would be difficult to develop using traditional platforms.
Beyond traditional models: the power of alternative data
Markets don’t move on financial indicators alone. Macroeconomic trends, consumer behavior, inflation metrics, and even commodity prices can offer powerful predictive insights.
Take, for example, the Tortilla Index—a novel approach that tracks the price of tortillas in Mexico against major stock prices like Chipotle (CMG) and Yum! Brands (YUM) to explore potential correlations between food inflation and fast-casual restaurant performance.
Building the Tortilla Index on Baselight
A financial analyst using Baselight can go beyond conventional models and test hypotheses with alternative data in real-time. Here’s how the Tortilla Index was created:
1. Data Discovery
The analyst begins by searching for relevant datasets in Baselight’s Data Catalog:
- Tortilla Prices in Mexico – A dataset tracking over 270,000 records from retail and mom-and-pop stores across 50,000+ cities.
- NASDAQ & NYSE Historical Prices – A dataset spanning decades of stock price movements, including major restaurant chains.
By integrating these two sources, from the Baselight data catalog, the analyst can test whether tortilla price fluctuations correlate with fast-casual restaurant stock performance.
2. Query Building
Using Baselight’s SQL-based Query Engine, the analyst writes a query that:
- Pulls daily tortilla prices and stock price data from different sources.
- Normalizes and adjusts for inflation, ensuring accurate comparisons over time.
- Joins both datasets, allowing direct correlation analysis between food prices and stock trends.
WITH stock_prices AS (
SELECT
date,
ticker,
"close"
FROM
@kaggle.eren2222_nasdaq_nyse_nyse_a_otc_daily_stock_1962_2024.nyse_1962_2024
WHERE
ticker IN ('CMG', 'YUM', 'EAT', 'SHAK')
),
tortilla_prices AS (
SELECT
CAST(year || '-' || month || '-' || day AS DATE) AS date,
AVG(price_per_kilogram) AS daily_tortilla_price
FROM
@kaggle.richave_tortilla_prices_in_mexico.tortilla_prices
GROUP BY
CAST(year || '-' || month || '-' || day AS DATE)
),
combined_data AS (
SELECT
sp.date,
AVG(CASE WHEN sp.ticker = 'CMG' THEN sp."close" END) AS CMG_price,
AVG(CASE WHEN sp.ticker = 'YUM' THEN sp."close" END) AS YUM_price,
AVG(CASE WHEN sp.ticker = 'EAT' THEN sp."close" END) AS EAT_price,
AVG(CASE WHEN sp.ticker = 'SHAK' THEN sp."close" END) AS SHAK_price,
tp.daily_tortilla_price
FROM
stock_prices sp
INNER JOIN tortilla_prices tp
ON sp.date = tp.date
GROUP BY
sp.date, tp.daily_tortilla_price
ORDER BY
sp.date DESC
LIMIT 500
)
SELECT
date,
CMG_price,
YUM_price,
EAT_price,
SHAK_price,
daily_tortilla_price*10 AS tortilla_price
FROM
combined_data
ORDER BY
date ASC;Now, the analyst has a clean, structured dataset linking fast-casual restaurant stock prices to tortilla inflation trends over time.
3. Visualization
Rather than relying solely on tables, Baselight allows analysts to build real-time visualizations for clearer insights.
Custom Chart Setup:
- X-Axis: Time (Date)
- Y-Axis 1: Tortilla Prices (Line Chart)
- Y-Axis 2: Stock Prices for CMG & YUM (Bar Chart)
With this chart, the analyst can instantly spots patterns—perhaps noticing that tortilla price surges precede stock price increases for major fast-casual chains, indicating a potential leading indicator for food inflation.
The analyst’s competitive edge
The Tortilla Index is just one example of how Baselight enables financial professionals to develop novel data-driven models.
Why Financial Analysts Use Baselight:
- Unified Data Access – Discover and integrate alternative datasets without complex setup.
- Seamless Querying – Combine financial and macroeconomic data in one place.
- Real-Time Visualization – Create custom charts for clearer market insights.
- Flexible Monetization – Analysts can publish and sell their own data models to others.
By eliminating the data silos and inefficiencies of traditional platforms, Baselight empowers analysts to move beyond conventional financial models and uncover new, high-value insights.
Start building today
The future of financial analysis isn’t just about crunching numbers—it’s about discovering new ways to interpret the world.
Want to test your own alternative market models? Join the Baselight waitlist today and start uncovering insights that others might be missing.
📩 Have a dataset you’d like to integrate? Reach out to us at hello@baselight.ai.