HiveQ infrastructure
Build, test, and monitor quantitative research on an access-controlled, 100% cloud-native platform. HiveQ runs the infrastructure while you control the strategy.
Inspect schemas, samples, symbols, and access before you commit research time.
Daily Bars
bars_1d60.6M rows · 4.8K symbols · updated 5d ago
Open an isolated notebook or development workspace without maintaining the server yourself.
import hiveq_data as hdimport pandas as pd raw = hd.Historical().get_data( dataset="HIVEQ_US_EQ", schema="bars_daily", symbols=["SPY"], start="2025-01-01", end="2025-06-30",)bars = pd.DataFrame(raw["data"])bars["return"] = bars["adj_close"].pct_change()Store tested Python functions by namespace and version. Load the same code in notebooks and research projects.
Registered functions
Source
calculate_sma.py
Review performance metrics, orders, trades, positions, and event logs with the completed run.
Monitor orders, positions, exposure, profit and loss, and account activity with virtual capital.
Realized +$44.4K
Unrealized +$8.2K
Fees $4.1K
Who does what
You control the code, the versions, and the parameters. You decide what moves to paper trading and, when available, live trading.
You control
Write the strategy. Select the data and the parameters that it uses.
Select the version of each software package or library.
Select which completed backtest goes to paper trading.
When this option is available, select the paper-trading run and the broker that you use.
HiveQ operates
managed platformHiveQ applies the access rules. HiveQ sends the market data to the app, the Python package, and the API.
HiveQ makes and operates your Jupyter, Web IDE, and Marimo workspaces. Each workspace is separate.
HiveQ runs the backtest. HiveQ keeps the results, the orders, the trades, the positions, and the logs.
HiveQ sends the market data, the order data, the positions, and the account values to the terminal.
Planned later
These items are not in the private preview.
Open HiveQ and start building your strategy.