Data Sources: Where to Get This Data

You now understand what alternative data matters: open interest, funding rates, liquidation levels, order flow. The next question is practical: where do you actually get this data, and how much does it cost?

Let me be completely honest about the landscape. Good data isn't cheap. But there are options at every budget level, and understanding the tradeoffs helps you make smart decisions.

Free Sources: Limited But Useful

Several platforms offer free tiers that provide valuable starting points.

Coinglass (coinglass.com) offers the best free access to derivatives data. You can see aggregated open interest, funding rates across exchanges, and liquidation data. The catch: limited historical depth, no API access, and you're looking at delayed snapshots rather than real-time streams.

Trading View has some built-in indicators for OI and funding on select exchanges. Good for visual analysis but not for systematic trading.

Exchange APIs themselves are free and provide their own data: Binance, Bybit, and others let you pull funding rates, OI, and liquidation streams directly. This requires technical ability to collect and store the data yourself.

The fundamental limitation of free sources: you get snapshots, not history. You can see current funding rates, but not funding rates for the past year. Without history, you can't backtest. Without backtesting, you can't validate edges.

Paid Sources: What's Worth the Money

If you're serious about building a signals engine, you'll need historical data. Here's an honest assessment of the major providers.

HyBlock Capital ($600/month) is what we use at TargetHit. It provides institutional-grade derivatives data across all major exchanges: comprehensive OI, liquidation levels, funding, and sophisticated metrics like CVD, order book depth, whale activity, and dozens of derived indicators. The API is robust, data quality is excellent, and historical depth goes back years. The price reflects the value. This is institutional-grade infrastructure.

Laevitas (varies) focuses on options and derivatives data. Strong on Greeks, implied volatility, and options flow. If you're trading options or using options data for directional signals, worth evaluating.

Glassnode ($30-800/month) specializes in on-chain analytics: realized price, SOPR, NUPL, supply metrics. Less relevant for short-term signals but valuable for understanding macro positioning and market structure.

CryptoQuant (similar range) competes with Glassnode on on-chain data, with some exchange flow metrics as well.

The Alpha Vantage and similar aggregators are cheaper but typically just aggregate exchange data without the sophisticated derived metrics that create edge.

API Considerations

When evaluating data providers, look beyond the headline data offerings:

Rate limits matter enormously. Can you pull data for 50 coins across multiple timeframes without hitting throttles? Some providers severely limit requests.

Historical depth determines what you can backtest. Two years minimum, three or more is better.

Data granularity affects what edges you can find. 1-minute data enables more precise signals than daily snapshots.

Reliability and uptime matter when you're running live systems. Missing data means missing signals.

Documentation quality affects development speed. Good docs save dozens of hours.

Cost/Benefit Analysis

Here's the uncomfortable truth: if you want institutional-grade data with the depth and breadth needed for serious edge discovery, you're looking at $500-1000+ per month.

Is it worth it? That depends on your capital and expected returns.

If you're trading a $100K account and your signals generate even 20% additional annual return from better data, that's $20K versus $7K in data costs. Clear ROI.

If you're trading a $5K account, the math doesn't work. Focus on free data and simpler strategies while you build capital.

The middle ground: start with free sources to learn and prototype. Validate your approach with whatever historical data you can scrape or obtain. Then invest in premium data once you have a system worth feeding.

Data Quality Issues to Watch

Not all data is equal, even from the same provider. Watch for:

Exchange inconsistencies: Different exchanges report metrics differently. Aggregated data smooths this but can mask important divergences.

Missing data: Gaps in the feed corrupt your backtests. Always check for completeness.

Stale data: Some free sources update infrequently. Real-time claims don't always mean real-time delivery.

Survivorship bias: Historical data might exclude delisted coins or failed exchanges, making past conditions look rosier than reality.

Look-ahead bias: Some calculated metrics inadvertently use future information. We discovered this with one of our indicators (ICS_26) that used future BTC returns in its calculation. Always validate exactly how derived metrics are calculated.

What We Recommend

For someone starting out: Coinglass free tier plus exchange APIs. This gives you enough to learn and prototype.

For someone building a serious signals engine: HyBlock or similar institutional data. The cost is real but the data advantage is real too.

For specific use cases: Layer in specialized providers. Glassnode for on-chain if that's your strategy. Laevitas for options data if you trade options.

Don't try to collect everything from every source. Pick one comprehensive provider and go deep on their data before adding complexity.

Key Takeaways

Free data is useful for learning but lacks the historical depth needed for backtesting. Premium data providers range from $30 to $800+ per month depending on depth and coverage. The right choice depends on your capital base and trading approach. Data quality issues like gaps, staleness, and look-ahead bias can corrupt your analysis. Start simple, validate your approach, then invest in better data as justified by returns.

Next, we'll cover how to actually build a data pipeline to ingest, store, and process all this information.