Primitive
Foundational financial sentiment. Early corpus establishing baseline lexicon popularity and term frequency distributions.
Latest Model: aurelius.v.1
Financial Sentiment
Analysis API
Transform financial text into actionable sentiment signals. Our proprietary model analyzes news articles and social media with institutional-grade accuracy for cross-sectional return predictability.
<50ms
Processing Time
4
Model Eras
Temporal Scoring
Language Evolves.
Language Evolves.
Our Model Knows.
The same text can have different meanings across time periods. Our era-aware scoring captures how financial language shifts.
💎
"Diamond hands! HODL to the moon! 🚀🚀🚀"
Confidence: 12%
era: primitive
<2016
-0.95
bearish
"Diamond", "HODL", "moon" absent from early financial lexicon. Only legacy terms scored, net negative sentiment.
Confidence: 78%
era: meme
2019-2023
+0.9999
very_bullish
WSB vernacular fully recognized. "Diamond hands", "HODL", "moon" carry strong bullish sentiment in meme-stock era.
{ "era": "meme" }
One parameter. Historically accurate sentiment.
Model Eras
Ramp
Rapid growth in corpus containing information pertaining to financial markets. Expansion of digital discourse.
Meme
The golden era of r/wallstreetbets and the GME short squeeze. Unprecedented social attention and retail flow.
Present
Post-meme era dilution of social tokens. Normalization and institutional adaptation to new paradigms.
The Model
Academic Research,
Academic Research,
Production Ready
Built on academic methodology from Paolucci et al. 2024, Discourses delivers research-grade sentiment analysis through a simple API.
aurelius.v.1
Discourses Model
Unlike LLMs and static sentiment engines, the Discourses Model explicitly captures time-variant sentiment. Financial language evolves—"disruption" meant crisis in 2008 but innovation in 2020. Our model adapts scoring to the temporal era, producing a unique measure of sentiment that contributes to return predictability.
Time-variant lexicons across eras
Cross-sectional return predictability
Interpretable, not black-box
Document Analysis
Process longer news articles and reports with comprehensive analysis.
Batch Processing
Analyze up to 100 texts in a single parallel request.
Real-Time Processing
Sub-50ms response times for breaking news and earnings releases.
Temporal Era Scoring
Period-appropriate lexicons spanning 10+ years of financial language evolution.
How it Works
From Text to
From Text to
Signal in Milliseconds
A simple REST API call is all it takes. Send your text, receive a probabilistic sentiment signal powered by aurelius.v.1.
1
Send Your Text
POST any financial text—news articles or social media posts—to our REST API.
2
aurelius scores
Our time-variant model analyzes your text using era-appropriate lexicons, applying CLT-based probability scoring.
3
Get Your Signal
Receive a JSON response with sentiment scores, confidence levels, and token-level breakdowns in under 100ms.
Pricing
Simple pricing
Simple pricing
for every scale
Start with our Hobbyist tier, scale as you grow. All plans include access to the Discourses Model.
Hobbyist
For testing and evaluation
Free
- 100 requests/day
- 5,000 character limit
- Single text analysis
- 2 API keys
Most Popular
Builder
For developers and researchers
$19
/month
- 5,000 requests/day
- 10,000 character limit
- Batch analysis (10 texts)
- 5 API keys
Professional
For teams and applications
$199
/month
- 50,000 requests/day
- 50,000 character limit
- Batch analysis (100 texts)
- Unlimited API keys
Methodology
Time-Variant
Time-Variant
Sentiment Modeling
A rigorous statistical approach to financial language that captures how word meaning evolves across market regimes.
Beyond Static Dictionaries
aurelius.v.1 explicitly models this temporal evolution by measuring each token's sentiment contribution at each point in time.
1
Aggregate Token Sentiment
For each word, compute the average sentiment of all documents containing it at time t.
2
Apply Central Limit Theorem
With sufficient observations, the sample mean converges to a normal distribution.
3
Derive Probabilities
Calculate the probability each token contributes positively or negatively to sentiment.
Why Time-Variance Matters
Unlike LLMs that use static embeddings trained on fixed corpora, aurelius.v.1 measures each word's sentiment charge in context of its era. This produces signals that are statistically grounded, temporally aware, and uniquely predictive of cross-sectional returns.
Mathematical Framework
Token Sentiment Mean
S̄w,t
=
1
nw,t
∑d
Sd
𝟙{w∈d}
S̄w,t = mean sentiment for token w at time t
nw,t = count of documents containing w
Sd = sentiment score of document d
𝟙{w∈d} = indicator: 1 if token w appears in doc d
CLT Convergence
S̄w,t
→
𝒩(μw,t,
σ²w,t
nw,t
)
𝒩 = normal distribution
μw,t = true mean sentiment of token w
σ²w,t = variance of sentiment for token w
Signal Probability
P(w+ | t)
=
Φ(
S̄w,t
σw,t / √nw,t
)
P(w+ | t) = probability token w is positive at time t
Φ = standard normal CDF
σ / √n = standard error of the mean
Φ is the standard normal CDF, yielding interpretable confidence levels for each token's sentiment contribution.
Coming Soon
Post API
Programmatically access and analyze market discussions from the Symposium. Stream real-time sentiment from traders and investors discussing specific instruments.
# Stream posts for an instrument
GET /api/v1/posts/stream?instrument=AAPL
{
"posts": [
{
"id": "disc_12345",
"instrument": "AAPL",
"content": "Strong earnings report...",
"sentiment": { "score": 0.84, "label": "bullish" },
"author": { "id": "usr_789" },
"timestamp": "2024-12-17T14:30:00Z"
}
],
"aggregate_sentiment": 0.72
}Real-Time Streaming
WebSocket streams of posts as they happen. Filter by instrument, author, or sentiment threshold.
Aggregate Sentiment
Pre-computed sentiment aggregates per instrument. Track sentiment shifts in real-time.
Historical Access
Query historical posts and sentiment data. Backtest strategies against crowd sentiment.
Low Latency
Sub-100ms delivery from post creation to your application. Built for algorithmic trading.
Risk Disclosure
Sentiment analysis is provided for informational purposes only. All investments carry risk. Past performance does not guarantee future results.
Read Full DisclosureResearch Disclosure
Discourses implements the methodology from Paolucci et al. 2024 research. No underlying academic data is used or distributed.
Read Full DisclosureImportant: Discourses is not a registered investment advisor, broker-dealer, or financial planner. The sentiment scores provided are analytical tools, not investment recommendations.