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Order Flow Data Exporter for Retail Quants
Build a SaaS layer on top of professional market data feeds that lets traders fetch futures tick and depth data, then export it into research-ready CSV or Parquet with symbol mapping and presets. The value is not replacing data vendors, but making their data immediately usable for strategy research by independent traders who are stuck on bar-based workflows.
为什么这很重要
You already have a working research setup built around minute-bar files, but the moment you want to test order flow ideas, the workflow breaks. The data you need lives in futures markets, arrives in formats designed for engineers, and comes with terminology that is easy to misuse if you trade CFDs or spot instruments. You are not only buying data; you are buying a way to avoid weeks of trial and error. Existing providers can deliver high-quality feeds, but they still leave you to figure out symbol selection, file conversion, and how to get something usable into your notebook or backtester.
- · 专为 Independent algorithmic traders and small quant teams who trade CFDs, futures, or FX but need exchange-based order flow data in a backtesting-friendly format. 打造。
- · 最可能的变现方式：SaaS subscription。
痛点叙事
You already have a working research setup built around minute-bar files, but the moment you want to test order flow ideas, the workflow breaks. The data you need lives in futures markets, arrives in formats designed for engineers, and comes with terminology that is easy to misuse if you trade CFDs or spot instruments. You are not only buying data; you are buying a way to avoid weeks of trial and error. Existing providers can deliver high-quality feeds, but they still leave you to figure out symbol selection, file conversion, and how to get something usable into your notebook or backtester.
得分构成
市场信号
Go-to-Market 启动方案
Solo Python-based traders currently using CSV bar data who want to test order flow strategies on equity index and metal futures within the next month.
~20K-50K active globally
SEO long-tail
$49/month
10 paying users who connect a vendor account and export at least 3 datasets within 30 days
MVP 方案 · 1-2 周
- Define a normalized schema for trades, quotes, and optional depth snapshots
- Build a command-line importer for one provider's historical futures dataset
- Create CSV and Parquet export jobs for ES, NQ, GC, and 6E
- Set up a basic web dashboard for symbol selection and date-range requests
- Write Python example notebooks showing immediate use in pandas and backtesting
- Add user accounts, saved export presets, and download history
- Implement spot-to-futures proxy guidance in the UI for FX and CFD users
- Add lightweight validation checks for missing sessions, rollover dates, and time zones
- Publish a landing page with sample files and a waitlist-to-paid conversion flow
- Run outreach to early users and measure export completion and repeat usage
差异化
为什么这件事可能失败
自我反驳——最重要的信任度信号
- 1Data licensing could prevent a commercially attractive packaging model, forcing the product into a narrower bring-your-own-vendor workflow.
- 2The best users may already be comfortable with APIs and see little reason to pay for conversion and packaging.
- 3Acquisition may be expensive because the buyer pool is specialized and fragmented across many small communities.
证据综述
AI 如何合成此洞察——无原话引用
The discussion consistently centers on the need for genuine exchange-based order flow rather than basic bars. Several participants pointed to a specialized data vendor as the practical choice, while multiple follow-up questions focused on file format, API access, and how to fit the data into an existing CSV workflow. That combination suggests a real opportunity in usability and workflow tooling rather than raw data creation.
行动计划
在写代码之前，先验证这个商机
推荐下一步
直接做
需求信号强烈。痛点真实、付费意愿明确——启动 MVP 开发。
落地页文案包
基于真实 Reddit 评论整理的即用文案，可直接粘贴到落地页
主标题
Order Flow Data Exporter for Retail Quants
副标题
Build a SaaS layer on top of professional market data feeds that lets traders fetch futures tick and depth data, then export it into research-ready CSV or Parquet with symbol mapping and presets. The value is not replacing data vendors, but making their data immediately usable for strategy research by independent traders who are stuck on bar-based workflows.
目标用户
适合：Independent algorithmic traders and small quant teams who trade CFDs, futures, or FX but need exchange-based order flow data in a backtesting-friendly format.
功能列表
✓ Connect to external historical futures data APIs ✓ One-click export to normalized CSV and Parquet ✓ Asset presets for indices, metals, commodities, and FX futures proxies ✓ Python-ready dataset schemas and sample loaders ✓ Usage-based download and storage management
去哪里验证
把落地页链接发布到 r/r/algotrading——这里就是这些痛点被发现的地方。
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