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DataAlpha
CatalogOrder FlowL1 & L2Derivatives & Market Data
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Market Data

Precise candlestick and VWAP/TWAP data for every instrument and interval.

Point-in-time candlestick and VWAP/TWAP data aggregated from raw exchange feeds. No survivorship bias, no look-ahead — just clean bars ready for research.

Market Data Datasets

Candlesticks

Tier 0

Open, High, Low, Close, Volume candlestick data with notional volume.

Key fields

OpenHighLowClose

VWAP / TWAP

Tier 0

Volume-weighted and time-weighted average prices for precise execution benchmarks.

Key fields

VWAPTWAP

Highlighted Metrics

vwap

VWAP

VWAP is the volume-weighted average price, emphasizing the levels where the market actually did the most business.

It is one of the most practical benchmarks in trading because it aligns naturally with execution quality and real turnover.

twap

TWAP

TWAP is the simple time-weighted average price, giving equal importance to the full interval path.

Comparing TWAP to VWAP helps reveal whether heavier trading occurred above or below the average timeline of prices.

Use Cases

Systematic strategy backtesting

Build and validate momentum, mean-reversion, and carry strategies on reliable price history without data leakage.

Cross-asset return indices

Construct factor indices spanning hundreds of instruments using consistent OHLCV aggregations across exchanges.

Charting & visualization tooling

Power dashboards and research notebooks with high-fidelity candlestick data at 1m–1d resolutions.

Volatility surface calibration

Use realized returns computed from clean close prices to calibrate implied-vol models and risk engines.

Execution benchmark construction

Compare fills against VWAP and TWAP benchmarks derived from the same raw tick data as your strategy.

Get started in minutes

pip install aperiodic
pypi ↗
example.py
from datetime import date
from aperiodic import get_metrics

df = get_metrics(
    api_key="your-api-key",
    metric="flow",
    timestamp="exchange",
    interval="1h",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT",
    start_date=date(2024, 1, 1),
    end_date=date(2024, 1, 31),
)

print(df.head())

Use with AI Agents

Query Market Data datasets programmatically with our Python SDK and REST API — built for autonomous research workflows.

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Institutional-Grade Order Flow, Liquidity & Derivative Metrics — Turn market microstructure into actionable signals, alpha and analytics in minutes.

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