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Point-in-Time Historical News Sentiment API
A SaaS API delivering pre-scored historical sentiment data with guaranteed point-in-time integrity, ticker mapping, and multi-year depth. Targets individual algo traders who need clean backtest data but cannot afford enterprise sentiment feeds or process terabytes of raw text. Differentiates through versioned article snapshots and built-in market-hours-aware timestamp alignment.
Why this matters
You are an individual algorithmic trader building sentiment-based strategies. You have free API access to Alpha Vantage and Finnhub, but their free tiers do not give you enough historical depth for a proper multi-year backtest. You discover raw sources like GDELT and Common Crawl, but processing 2.5 terabytes of text is impractical on your machine. Commercial sentiment services exist, but their subscription costs eat directly into whatever trading edge you might find. Even when you do get data, you discover that most providers hand you the current version of articles, not the original published version, introducing invisible look-ahead bias into your backtest. You spend weeks manually aligning sentiment timestamps with price bars, only to find your strategy has no genuine edge — and you wonder if the data quality issues were partly to blame.
- · Built for Individual algorithmic traders and small quant teams who run multi-year backtests on sentiment-based strategies but lack enterprise data budgets or ML infrastructure to process raw news archives.
- · Most likely monetization: SaaS subscription with tiered API limits.
The Pain · Narrative
You are an individual algorithmic trader building sentiment-based strategies. You have free API access to Alpha Vantage and Finnhub, but their free tiers do not give you enough historical depth for a proper multi-year backtest. You discover raw sources like GDELT and Common Crawl, but processing 2.5 terabytes of text is impractical on your machine. Commercial sentiment services exist, but their subscription costs eat directly into whatever trading edge you might find. Even when you do get data, you discover that most providers hand you the current version of articles, not the original published version, introducing invisible look-ahead bias into your backtest. You spend weeks manually aligning sentiment timestamps with price bars, only to find your strategy has no genuine edge — and you wonder if the data quality issues were partly to blame.
Score Breakdown
Market Signal
Go-to-Market
Individual algorithmic traders running Python-based backtests with sentiment signals who currently rely on free API tiers and need multi-year historical depth
~30,000 active globally based on community engagement in algorithmic trading forums and growing retail algo trading adoption
Organic posts in algorithmic trading communities with technical deep-dive content showing point-in-time bias problems
$29/month for 5-year historical API access with 10K calls
25 paying users within 30 days from a single community launch post demonstrating look-ahead bias in existing free data
MVP Scope · 1–2 weeks
- Set up GDELT BigQuery integration to pull 5 years of financial news with tone scores for top 50 megacap tickers
- Build ticker-mapping pipeline that associates GDELT articles with stock symbols using entity extraction
- Create point-in-time versioning logic that snapshots article content and sentiment at first-seen timestamp
- Design REST API schema with endpoints for historical sentiment by ticker, date range, and granularity
- Deploy basic FastAPI service on a cloud VM with PostgreSQL time-series storage for initial dataset
- Implement market-hours-aware alignment that maps sentiment timestamps to next tradable price bars
- Add FinBERT scoring pipeline for articles where GDELT tone scores are absent or low-confidence
- Build simple dashboard showing data coverage by ticker, date range, and source breakdown
- Create pricing tiers and Stripe integration with metered API usage limits
- Write a technical blog post demonstrating look-ahead bias in free-tier data and how the service fixes it
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1News content licensing costs may be prohibitively expensive for commercial redistribution, making the $29/month price point unsustainable and forcing prices into the range users already consider too costly
- 2GDELT and Common Crawl are genuinely free and improving; a sufficiently determined user can build their own pipeline with open-source tools, limiting the addressable market to those who value convenience over cost
- 3The core user behavior is episodic backtesting rather than continuous usage — users may subscribe for one month, run their backtest, find no edge, and churn, making lifetime value too low to cover customer acquisition costs
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Approximately six commenters discussed the difficulty of obtaining affordable, deep historical sentiment data. Multiple users cited free API tiers as insufficient for multi-year backtests. One commenter emphasized that point-in-time integrity is a critical but overlooked problem, as most sources provide current article versions rather than original published versions. Another user described the tricky alignment between sentiment and price indices and discovered a drift bug in their own pipeline. The discussion reveals that existing solutions split between free-but-raw sources requiring heavy processing and paid services that erode trading profits.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
Point-in-Time Historical News Sentiment API
Sub-headline
A SaaS API delivering pre-scored historical sentiment data with guaranteed point-in-time integrity, ticker mapping, and multi-year depth. Targets individual algo traders who need clean backtest data but cannot afford enterprise sentiment feeds or process terabytes of raw text. Differentiates through versioned article snapshots and built-in market-hours-aware timestamp alignment.
Who It's For
For Individual algorithmic traders and small quant teams who run multi-year backtests on sentiment-based strategies but lack enterprise data budgets or ML infrastructure to process raw news archives
Feature List
✓ Pre-scored historical sentiment API with 5+ years of depth ✓ Point-in-time article versioning to prevent look-ahead bias ✓ Automatic ticker-to-news mapping with corporate action awareness ✓ Market-hours-aware timestamp alignment (next tradable bar mapping) ✓ Tiered pricing starting at an affordable level for individual traders ✓ Raw news + score delivery option for users who want custom models ✓ Ticker-filtered historical news delivery via API (query by symbol and date range) ✓ Point-in-time article content with original publication timestamps
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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