Normalized schema
Every asset class shares the same fields, so notebooks and pipelines generalize across markets.
Solutions
Researchers
Remove weeks of data-engineering work with clean, normalized historical and live data from one API and one schema.
A single normalized dataset for reproducible market research.
Historical depth
Backtest and study 12+ years of tick and bar data without stitching multiple vendors together.
Reproducible pipelines
Deterministic timestamps and stable identifiers make results easy to reproduce and share.
Academic-grade multi-asset data specifications
| Research domain | Data granularity and depth | Example research topics |
|---|---|---|
| Market microstructure | 12+ years of raw tick data for major global Forex markets (150+ currency pairs), commodities and crypto, with L1/L2 order-book depth. | Order-flow imbalance (OFI), liquidity depletion and high-frequency limit-order-book (LOB) modelling. |
| Asset pricing and quantitative factors | Multi-interval OHLCV and volume-weighted average price (VWAP) data for major global equities, including US and Hong Kong stocks, and key derivatives indices. | Momentum effects, multi-factor models (Fama-French) and cross-asset risk-premium research. |
| Behavioural finance and volatility | Second-by-second change histories and records of sharp volume fluctuations for 1,000+ spot and perpetual-contract pairs worldwide. | Crypto-market extreme-event resonance, abrupt shifts in market sentiment and tail risk. |
| Macroeconomics and cross-market transmission | Long time series for core commodities, including gold, silver and Brent crude, and major global equity indices. | Inflation-expectation transmission and the real-time response of commodity futures to macroeconomic policy. |
Why financial researchers and laboratories choose AllTick
Eliminate tedious data engineering and focus on theoretical innovation
In the past, empirical finance research often required weeks or months to buy, download and clean data from different exchanges and vendors. AllTick removes that barrier. Our unified cross-market schema normalizes complex international, multi-asset data into a standard format with consistent fields and UTC timestamps, so researchers can devote their full attention to econometric modelling and paper writing.
Deterministic timestamps for perfectly reproducible results
Academic research places strict demands on reproducibility. AllTick historical time series provide high-precision, millisecond deterministic timestamps and exclude bias caused by bad data, spikes or exchange transmission delays. Peers and team members using the same code and the AllTick API can reproduce identical empirical results.
Native integration with the research technology stack
Whether you analyse data in Jupyter Notebook with Pandas and NumPy, build complex GARCH volatility models in R, or use MATLAB for matrix computation, AllTick standard REST API lets you retrieve large datasets asynchronously in only a few lines of code.
Dedicated academic offers and flexible distribution licences
We understand the needs of academic research and laboratory budgets. AllTick offers cost-effective academic sponsorship programmes for university professors, doctoral students and financial laboratories, with flexible licences that support sharing cleaned research samples within teams and research groups to speed paper publication.
Jupyter Notebook data-import example for researchers
Scenario: retrieve historical K-line data in one call for time-series regression analysis (Python example).
# AllTick — Real-time Financial Market Data API
# Real-time Forex, Stocks, Crypto, Commodities and Indices market data
import json
import os
import pandas as pd
import requests
def load_research_dataset(symbol, query_count=100, end_timestamp=0):
"""Load AllTick daily stock candles into a UTC-indexed Pandas DataFrame."""
query = {
"trace": "research_daily_kline",
"data": {
"code": symbol,
"kline_type": 8, # Daily candle
"kline_timestamp_end": end_timestamp, # 0 requests the latest candle
"query_kline_num": query_count, # Maximum 500 per request
"adjust_type": 0,
},
}
response = requests.get(
"https://quote.alltick.co/quote-stock-b-api/kline",
params={
"token": os.environ["ALLTICK_API_TOKEN"],
"query": json.dumps(query, separators=(",", ":")),
},
timeout=15,
)
response.raise_for_status()
payload = response.json()
if payload.get("ret") != 200:
raise RuntimeError(payload.get("msg", "AllTick K-line request failed"))
frame = pd.DataFrame(payload["data"]["kline_list"]).rename(
columns={
"open_price": "open",
"close_price": "close",
"high_price": "high",
"low_price": "low",
}
)
numeric_columns = ["open", "close", "high", "low", "volume", "turnover"]
frame[numeric_columns] = frame[numeric_columns].apply(pd.to_numeric)
frame["timestamp"] = pd.to_datetime(pd.to_numeric(frame["timestamp"]), unit="s", utc=True)
return frame.set_index("timestamp").sort_index()
if __name__ == "__main__":
daily_candles = load_research_dataset("AAPL.US")
print(daily_candles.tail())Start streaming market data today
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