How to Pick a Data Feed for Your Trading Bot (2026 Decision Guide)

You’re building a trading bot. Before you write the strategy code, you have to pick a market data feed. The wrong choice locks you into expensive monthly fees, slow APIs, or missing data shapes you only discover after weeks of work. This guide walks through the questions to ask, the trade-offs, and a concrete decision tree.

Step 1: Define what your bot actually needs

The right data feed depends entirely on the bot. Answer these five questions first:

1. What time scale does it trade? Daily-close trades, intraday, scalping, HFT?
2. What asset classes? US equities only, options, futures, crypto, forex?
3. What history depth do you need for backtesting? 5 years, 20 years, 30+ years?
4. Do you need pre-computed factors (RSI, regime, VIX buckets) or will you compute them yourself?
5. Will you be running AI/agent-driven decision logic (Claude, Cursor, ChatGPT)?

If you can’t answer all five, pause. Pick a free or cheap data feed for prototyping (yfinance or Alpha Vantage free tier), build a minimum-viable bot, and learn what you actually need. Premature optimization on data infrastructure is the most common time sink in retail quant.

Step 2: Match your needs to a category

If you trade real-time or intraday

You need a feed with sub-second latency and tick-level access. The serious choices: Polygon.io / Massive (real-time on Advanced tier, $199/mo), Alpaca Markets (real-time with brokerage integration), or Interactive Brokers if you’re using IB for execution anyway.

Avoid: free tiers (yfinance, Alpha Vantage free) for real-time. They’re not built for it.

If you trade end-of-day or longer

End-of-day feeds are cheaper, more reliable, and sufficient for the majority of retail quant strategies. Options:

• Tiingo ($30/mo individual): Best cheap-and-reliable. 30+ years of EOD across 100K+ securities. No bells and whistles.
• TradeOdds ($29/mo Pro, $99/mo Power User): Same OHLCV depth as Tiingo, plus 16 pre-computed factor columns. Power User adds direct SQL access. If your strategy needs RSI buckets, regime classifications, or pre-aligned forward returns, this saves weeks of pipeline work.
• Massive (Polygon) Starter ($29/mo): Same price as Tiingo, less history (5 years).

If you need fundamentals (earnings, balance sheets)

Add Financial Modeling Prep ($20-$200/mo depending on tier) or include Tiingo’s fundamentals add-on. TradeOdds covers earnings proximity as a bucket but does not provide full fundamental statements.

If your bot is AI-driven (Claude / Cursor / ChatGPT)

Look for MCP server availability. As of mid-2026, TradeOdds is the only stock data vendor with a published MCP server (tradeodds-mcp on npm). Other vendors will catch up, but for now this is the only path to a Claude-grounded bot without writing your own MCP server.

If you don’t pick TradeOdds for the data, plan to write your own MCP wrapper around your chosen vendor’s REST API. Budget a few days for that.

If you want to backtest cheaply

You can use yfinance (free) for prototyping. It’s unreliable enough that you should not deploy a live bot against it, but for offline backtesting it’s adequate. Pair with one of the paid options above for the production data path.

Step 3: Decision tree

Real-time or intraday execution?
├─ YES → Massive Advanced ($199/mo) or Alpaca
└─ NO ↓

Need pre-computed factors (RSI buckets, regime, forward returns)?
├─ YES → TradeOdds Pro ($29) or Power User ($99) for SQL
└─ NO ↓

Need broad global symbol coverage (international, mutual funds)?
├─ YES → Tiingo Power ($30/mo)
└─ NO ↓

Need fundamentals (earnings, balance sheets)?
├─ YES → Financial Modeling Prep + Tiingo
└─ NO ↓

Need an MCP server for Claude / Cursor?
├─ YES → TradeOdds Pro ($29) or Power User ($99)
└─ NO ↓

Just prototyping?
└─ yfinance (free) for testing; pick a paid feed before going live

Step 4: Account for hidden costs

Sticker price isn’t total cost. Add:

• Engineering time to compute factors. If you pick a raw-OHLCV feed and need RSI buckets, regime classifications, and forward returns, that’s a 1-2 week pipeline. Add the equivalent of a month’s developer salary to the data cost.
• Storage and ingestion if self-hosting. Running your own ingestion against any REST API means a Postgres or Parquet pipeline. Add a managed-DB or storage cost.
• Reliability cost. Free feeds (yfinance) go down. If your bot can’t trade because data is missing, that has a real cost.
• MCP/AI integration cost. If you need MCP and your vendor doesn’t have one, budget 2-5 days to build and maintain your own.

For a typical retail bot: TradeOdds Pro ($29) skips weeks of factor pipeline work and ships the MCP server out of the box. Tiingo ($30) is cheaper on data but you’ll write all the analytics yourself. The “right” choice depends on whether your time is more valuable than $29 a month.

Step 5: Build a minimum-viable bot before scaling up

Whichever feed you pick, get a small, ugly version of the bot running first. It should:

• Connect to the data feed
• Run one strategy on one symbol
• Place paper trades into your chosen broker
• Log results to a file or database

Only after this works should you scale up to more symbols, more sophisticated logic, or higher tiers. Most retail quant projects die because the builder over-engineered the data layer before ever placing a paper trade.

A common 2026 pattern

For an AI-driven retail trading bot in 2026, the modal stack we see is:

• Data: TradeOdds Pro ($29) or Power User ($99) for the OHLCV + pre-computed factors + MCP server.
• Brokerage: Alpaca for commission-free equities execution.
• Bot runtime: A Python script triggered by a cron or scheduler on a $5-$10/mo VPS.
• AI layer: Claude Desktop (during development, via MCP) for testing setups; the Anthropic API for production decision logic.
• Storage: SQLite or Postgres for trade history and audit logs.

Total infrastructure cost: ~$50-$130/month. That’s it. The big cost is your time figuring out which strategy works — not the data.

---

Verification. Pricing tiers and feature counts verified on June 4, 2026. Comparisons against specific vendors are in our Compare hub.

Disclaimer. TradeOdds provides historical analysis for informational purposes only. This is not investment advice. Past performance does not guarantee future results.