Research-to-trading workflow
The platform combines research, backtesting, and live execution in one workflow, so users can move from idea testing to broker-connected trading without switching tools.
Scalar Fieldは、株式・オプション・予測市場・トークン化資産に対応し、量的研究、バックテスト、証券会社接続取引を行うAIトレーディングデスクです。Pythonで市場データと執行ワークフローにアクセスできます。
Scalar Field is an AI agentic trading desk built for quantitative research and execution. It combines market data access, backtesting, strategy automation, and brokerage-connected trading in a single Python-first platform.
The site positions the product around a research-to-live-trading workflow: users can pull datasets, test strategies, and deploy agents across equities, options, prediction markets, and tokenized assets. Documentation examples show direct use of scalarlib functions such as getOHLCV() and getOptionsQuotes() to retrieve market data for analysis and trading workflows.
The platform’s pricing page also shows plan-based access with Pro, Ultra, and Enterprise tiers. Those tiers add higher usage limits, automation features, premium support, custom integrations, and deployment or security options for larger teams.
The platform combines research, backtesting, and live execution in one workflow, so users can move from idea testing to broker-connected trading without switching tools.
Documentation exposes Python functions such as `getOHLCV()` for equity and ETF bars and `getOptionsQuotes()` for options quotes, showing a programmatic way to request market data.
The pricing page lists a broad set of datasets, including equities, options, earnings, insider and congressional trades, institutional holdings, analyst ratings, prediction markets, and FRED series.
Trading integrations include Alpaca, Robinhood, Polymarket, and Jupiter DEX, with additional venues listed as coming soon and custom brokerage integrations available on Enterprise.
Pricing tiers add larger context windows, longer compute time, automation tools, premium support, and, on Enterprise, private workspaces, SSO/SAML/RBAC, and private cloud or VPC deployment.
The docs describe live, intraday, and historical data modes, with examples for daily, minute, and hourly requests, which supports both long-horizon analysis and shorter-term signal work.
Use the platform to pull historical daily, hourly, or minute bars for equities and ETFs, then test signal ideas or portfolio rules before connecting them to live execution.
Query options quotes with bid/ask/mid pricing, contract metadata, Greeks, and implied volatility to study options surfaces or build derivatives models.
Connect brokerage accounts or supported venues to automate order placement for strategies that have already been researched and validated.
Monitor prediction markets, live quote snapshots, and market data updates to track short-term opportunities across different venues and asset types.
Use Enterprise features when a team needs custom brokerage integrations, private workspaces, security controls, or private cloud deployment.
Scalar Field is a quantitative research platform that supports research, backtesting, and live trading through a Python library called `scalarlib`. The documentation also shows market data access and brokerage-connected trading workflows.
The docs present Python examples for accessing datasets through functions such as `getOHLCV()` and `getOptionsQuotes()`. That indicates a code-first workflow rather than a no-code trading interface.
The pricing page lists a Pro plan, an Ultra plan, and an Enterprise plan. Ultra adds higher usage limits and premium support, while Enterprise adds custom integrations, security options, and private deployment capabilities.
The site shows trading connectivity for Alpaca, Robinhood, Polymarket, and Jupiter DEX, with Public.com and Webull listed as coming soon. The Enterprise plan also mentions custom brokerage integrations such as Interactive Brokers, Ameritrade, and Schwab.
The docs page says the platform provides compute, data, and infrastructure for live trading through a simple Python library, and the market-data pages show datasets for equities, options, and other reference data. The exact breadth of access depends on the dataset or plan.
トラフィックデータは参考情報としてご利用ください。
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