iturri 0.2.3

Client for the Iturri verified market-data API — quality-flagged candles, funding, open interest, order flow and ML-ready bundles for trading bots and AI agents. MCP + x402 native.

iturri

Python client for the Iturri verified market-data API — historical market data for trading bots, backtests, and AI agents where every bar carries a quality flag (verified · raw · reconciled · interpolated · disputed · outage) and every candle is traceable to a checksummed primary source.

• 124M+ quality-flagged OHLCV bars: 34 crypto symbols (1m/5m/15m/1h/1d since 2015) + 86 US stocks & ETFs (consolidated-tape EOD since 2016, as-traded and split-adjusted bases)
• Funding rates, open interest, taker order flow, curated events, market context (Fear & Greed, VIX, DXY…)
• Leakage-safe feature matrices, multi-timeframe aligned bundles, BSQ token sequences, training packs
• Agent-native: 11 MCP tools and x402 micropayments (USDC on Base) — no account, no API key

Zero hard dependencies. With pandas installed you get DataFrames; without it, columnar dicts.

Install

pip install iturri            # or: pip install "iturri[pandas]"

Quickstart

import iturri

iturri.catalog()                          # free discovery — symbols, ranges, pricing

iturri.token = "…"                        # membership token from iturri.ai/subscribe
                                          # (or: export ITURRI_TOKEN)

df = iturri.bars("BTC", "1h", start="2024-01-01", end="2024-02-01")
df = df[df.quality == "verified"]         # the whole point

feats = iturri.features("SPY", "1d_split", start="2026-06-01")
iturri.bundle("crypto")                   # signed training-pack URLs

Without a token, priced endpoints return HTTP 402 with an x402 quote — payment-capable agents settle per call ($0.001 per 1,000 candles) with no human in the loop.

Full surface

catalog · bars · features · funding · open_interest · orderflow · context · events · regime · validate · bundle — mirroring the 11 MCP tools at https://iturri.ai/mcp.

validate(symbol, tf, start=…, end=…) returns a data-quality report (coverage, gaps, flag distribution) so you can audit a range before backtesting on it.

For AI agents (MCP)

{ "mcpServers": { "iturri": { "type": "http", "url": "https://iturri.ai/mcp" } } }

describe_catalog is free forever. See the MCP tutorial.

Links

Docs: https://iturri.ai/#docs · OpenAPI: https://iturri.ai/openapi.json · llms.txt: https://iturri.ai/llms.txt · JS SDK + CLI: npm i iturri · Sourcing & verification policy: https://iturri.ai/sourcing

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Iturri sells data and tooling only. Nothing in any dataset, feature, label, or tool is a trading signal, a prediction, or financial advice.