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For Researchers

PriceHub was designed not only for traders but also for quantitative-finance researchers who need a reproducible, citable, and venue-agnostic data layer.

Why a Unified Layer Matters for Research

Cryptocurrency markets are fragmented across dozens of venues. Each exchange exposes its own API conventions — endpoints, rate limits, timestamp units, symbol notation, pagination, schema of returned fields. A typical research workflow that compares price discovery or liquidity across venues spends 50–80% of its setup time reconciling these differences before any analysis can begin.

PriceHub absorbs that friction. A single function signature returns a normalized pandas DataFrame, so cross-venue studies become a for loop over broker identifiers rather than a separate integration project per exchange.

Research Use Cases

Cross-Exchange Price Discovery

import pandas as pd
from pricehub import get_ohlc

venues = ["binance_spot", "bybit_spot", "coinbase_spot", "okx_spot", "kraken_spot"]
closes = pd.concat(
    {v: get_ohlc(v, "BTCUSDT", "1h", "2024-01-01", "2024-12-31")["Close"] for v in venues},
    axis=1,
)
spread = closes.max(axis=1) - closes.min(axis=1)

The resulting series quantifies cross-venue spread, useful in studies of market efficiency and arbitrage decay.

Backtesting Strategy Portability

Strategies developed against a single exchange often fail to replicate when ported to another venue due to subtle data differences. PriceHub lets researchers run an identical backtest pipeline across venues and report robustness as a multi-venue distribution rather than a single point estimate.

Spot–Futures Basis Studies

spot = get_ohlc("binance_spot", "BTCUSDT", "1h", "2024-01-01", "2024-12-31")
fut = get_ohlc("binance_futures", "BTCUSDT", "1h", "2024-01-01", "2024-12-31")
basis = (fut["Close"] - spot["Close"]) / spot["Close"]

Basis behavior across regimes is a common research target; PriceHub aligns the indices so the subtraction is well-defined.

Market Microstructure Around Macroeconomic Releases

Tick-coarse OHLC at 1-minute intervals around FOMC or CPI release timestamps lets researchers measure realized volatility, jump frequencies, and order-flow imbalance proxies across venues.

Reproducibility Notes

  • Determinism: get_ohlc returns the same DataFrame given the same arguments, provided the exchange has not amended its historical data (rare but documented for some venues).
  • Date alignment: All timestamps are returned in UTC with explicit Open time indexing, eliminating timezone ambiguity.
  • Version pinning: For published research, pin pricehub to a specific version (pip install pricehub==X.Y.Z) and cite that version (see Citation).
  • Data provenance: PriceHub returns the raw API response with minimal transformation; field names and units come directly from the exchange documentation.

Citing PriceHub

If you use PriceHub in academic work, please cite the specific version you used. See the Citation page for the BibTeX entry and DOI.

Contributing Research Examples

If you have a published study that used PriceHub, please open an issue or a pull request adding it to this page. Real-world citations help us position the package in the broader quantitative-finance ecosystem.