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[Oluti]

That’s a really good question. Finding trustworthy market data is such a vital part of making any algorithmic trading work smoothly. Here’s what I’ve learned from experience and the industry over the years:

First off, many traders get their live data directly from their broker’s API. It’s pretty handy because it’s integrated and quick, but the quality can vary quite a bit depending on the broker. So, it’s a good idea to double-check that the data you’re getting is accurate and consistent.

For more serious research and backtesting, many people turn to premium data providers like Bloomberg, Thomson Reuters, Quandl, or TickData. They tend to have cleaner, more reliable historical data, though they can be costly. Still, they’re worth it if you need precision and depth.

There are also more affordable APIs from platforms like Alpha Vantage, IEX Cloud, or Polygon.io. These are good for real-time and historical data, but they often have limits on how much you can request. If you’re doing high-frequency trading, you might need to combine these sources or consider paid plans to get faster, more reliable data.

When it comes to data quality and latency, a few best practices help:

• Use multiple sources and cross-reference data to catch discrepancies.
• Implement validation checks to flag anomalies or missing data.
• Save raw data for audits or reprocessing if needed.
• For strategies sensitive to delays, consider colocating your servers near the exchanges or using direct exchange feeds if possible.

Dealing with bad or noisy tick data is another challenge. Techniques like filtering out outliers, smoothing data, or applying real-time corrections can really improve the quality. Some traders also subscribe to exchange correction feeds or apply their own adjustments.

Lastly, it’s important to find a balance between cost and reliability based on your trading style. High-frequency strategies often justify investing in direct exchange feeds, while less frequent trading might do just fine with premium APIs or broker data.

In summary, combining different sources, validating data thoroughly, and understanding each source's limitations are key to running a robust trading system.

Hope that helps, and happy trading!

 

[lioralace]

Alright, let’s be real for a sec—if your market data sucks, your algorithmic trading system’s pretty much toast. I mean, you ever tried backtesting a hotshot model on dirty data? You’ll cry. Facts. So, yeah, let’s talk about how not to get dumpstered by wonky price feeds and how to actually trust the numbers getting pumped into your shiny algo.

### Broker APIs: Easy, but Don’t Get Too Cozy

So, most folks just plug in whatever API their broker dishes out and hope for the best. Can’t blame them, it’s stupid simple to set up, and everything just works… until it totally doesn’t. Seriously—brokers vary wildly. Some are tight, some just cobble together Frankenstein feeds from who-knows-where. If you actually care about your money, cross-check with another source. I don’t care if it’s your cousin’s Yahoo Finance account—just compare. Saves you rage-quitting over trades made on glorified guesswork.

### Premium Data: Bring Your Wallet

Now, if you’re serious—like, you’ve got Bloomberg on speed-dial and your cat’s named Reuters—premium providers are the way. Bloomberg, TickData, whatever. Those folks sweat over their data so you don’t have to. Downside? They'll charge you like you’re buying a second-hand yacht. But for deep backtesting or high-stakes stuff, that cash is basically insurance against data-induced facepalms. It’s mostly worth it. Unless you’re just gambling Tesla YOLO options, then maybe just don’t.

### Cheap APIs: Not Bad, Actually

Not everyone’s rolling in hedge fund money. For most people messing around or building proof-of-concepts, Alpha Vantage, IEX Cloud, Polygon.io—solid picks. They’ve got free tiers, good enough for lots of stuff. Just remember, you get throttled to death if you ask for too much too fast. Want to go nuts? Upgrade or mash a couple together so you don’t hit those annoying limits all the time.

### Data Quality? Paranoid Pays Off

It’s like this—trust, but verify (shout out, Reagan). If you see prices that look like they came from another planet, check other feeds. Throw some basic logic in your code: missing timestamps? Weird gaps? Price jumps that make zero sense? Flag ‘em. Also, keep the raw stuff backed up. That way, if your data suddenly does a Beyoncé and goes all wild, you can audit and not just sit there sobbing.

If your strategy cares about speed (hi, HFT crew), colocate those servers. Seriously. Or pony up for direct exchange feeds. It’s not cheap, but a few milliseconds sometimes decides whether you eat steak or ramen that week.

### Tick Data: Be Ready to Wrestle

Tick data sounds cool—until you see it in the wild. It’s a junkyard. Outliers, bad ticks, timestamps from the Twilight Zone. Filter ruthlessly. Smooth the wild swings. Use corrections when you can. Build sanity checks that actually do some work. Don’t just trust the feed.

### Price vs. Quality: Pick Your Poison

End of the day, it’s your game. Hardcore quants with caffeine IVs and tradfi backgrounds? Gonna fritter away on exchange feeds and private networks. Casual swing traders or people doing research? Broker APIs and a paid data sub might be more than fine.

—

Bottom line: Good market data’s either saving your butt or setting your portfolio on fire. Mix up your sources, cross-check religiously, and obsess over weird blips until you’re sick of it—that’s how you build an algo you can trust. Don’t cheap out on the data pipeline and expect magic.

Good luck out there. And try not to blow up your account on sketchy ticks.