RETAIL INVESTOR DATA

Retail Investor Sentiment

Exclusive retail investor sentiment from the largest social finance platform.

426M MESSAGES · SINCE 2009JSON · CSV · ParquetReal-time (WebSocket) or daily batch

426M+

Messages

18 yrs

Continuous history

250+ TB

Raw corpus

3-layer

Auditable signal

Download Package Contents Build process Verification Audit trail Use cases Delivery Further reading Request a sample

01Download

Inspect a real sample

Representative records in the delivery format, ready to inspect before licensing the full dataset.

Raw message, author-labeled (firehose)

Representative shape, not real data. sentiment is the author own label at post time - the ground truth, not inferred.

messages.jsonlrepresentative

{
  "id": 582914037,
  "body": "$NVDA incredible earnings beat. Blackwell demand is insane.",
  "created_at": "2026-02-27T14:23:11Z",
  "sentiment": "Bullish",
  "user": { "id": 1847293, "username": "<handle>", "followers": 12840, "ideas": 8741, "join_date": "2015-03-12" },
  "symbols": ["NVDA"],
  "likes_total": 312,
  "replies": 47
}

Curated message, relinked symbol

Representative. Shows the curation and symbol-mapping provenance that makes every downstream signal row auditable.

curated/messages.jsonlrepresentative

{
  "record_id": "post:1lpgxpa",
  "source_platform": "<social-finance platform>",
  "created_at_utc": "2025-07-01T23:34:45Z",
  "body_or_title": "QQQ calls printing today",
  "symbols_detected": ["QQQ"],
  "requested_symbol": null,
  "mapping_method": "alias_relinked",
  "mapping_confidence": 0.65,
  "finance_intent_score": 0.68,
  "curation_band": "strict",
  "curation_bucket": "signal_keep"
}

Signal panel row, one symbol-day (the quant product)

Representative. One row per symbol-day; trade_date makes it look-ahead-free for backtesting.

security_day_signal.jsonlrepresentative

{
  "date": "2023-01-02",
  "symbol": "AAPL",
  "mention_count": 13,
  "weighted_mention_count": 6.96,
  "unique_author_count": 5,
  "sentiment_score": 0.123,
  "conviction_score": 0.236,
  "composite_signal": 0.324,
  "confidence_score": 0.617,
  "avg_spam_probability": 0.005,
  "trade_date": "2023-01-03",
  "trade_lag_days": 1,
  "company_name": "Apple Inc."
}

02Schema

Record shape

Every field, its type, whether it can be null, and a representative value.

date

required

Trading date for the symbol-day observation.

Type: date

Example 2023-01-02

symbol

required

Ticker the row aggregates.

Type: string

Example AAPL

mention_count

required

Raw message mentions that day.

Type: int

Example 13

weighted_mention_count

required

Mentions weighted by message and author quality.

Type: float

Example 6.96

unique_author_count

required

Distinct authors mentioning the symbol.

Type: int

Example 5

sentiment_score

required

Quality-weighted continuous sentiment.

Type: float
Unit: 0..1

Example 0.123

conviction_score

nullable

Conviction from engagement and author authority.

Type: float

Example 0.236

composite_signal

nullable

Composite multi-feature daily signal.

Type: float

Example 0.324

confidence_score

required

Coverage and quality confidence for the row.

Type: float

Example 0.617

avg_spam_probability

required

Mean spam-model probability across the day messages.

Type: float
Unit: 0..1

Example 0.005

trade_date

required

First date the signal is tradeable - makes the panel look-ahead-free.

Type: date

Example 2023-01-03

trade_lag_days

required

Lag from signal date to tradeable date.

Type: int
Unit: days

Example 1

company_name

nullable

Security-master join for the ticker.

Type: string

Example Apple Inc.

Field	Type	Constraint	Description
date	date	required	Trading date for the symbol-day observation. e.g. 2023-01-02
symbol	string	required	Ticker the row aggregates. e.g. AAPL
mention_count	int	required	Raw message mentions that day. e.g. 13
weighted_mention_count	float	required	Mentions weighted by message and author quality. e.g. 6.96
unique_author_count	int	required	Distinct authors mentioning the symbol. e.g. 5
sentiment_score	float · 0..1	required	Quality-weighted continuous sentiment. e.g. 0.123
conviction_score	float	nullable	Conviction from engagement and author authority. e.g. 0.236
composite_signal	float	nullable	Composite multi-feature daily signal. e.g. 0.324
confidence_score	float	required	Coverage and quality confidence for the row. e.g. 0.617
avg_spam_probability	float · 0..1	required	Mean spam-model probability across the day messages. e.g. 0.005
trade_date	date	required	First date the signal is tradeable - makes the panel look-ahead-free. e.g. 2023-01-03
trade_lag_days	int · days	required	Lag from signal date to tradeable date. e.g. 1
company_name	string	nullable	Security-master join for the ticker. e.g. Apple Inc.

03What's included

Firehose

Full message stream with user metadata, tickers, sentiment flags, and activity (likes, reshares, follows).

Symbol Event

Ticker-level engagement - message volume, likes, pageviews, watchlist changes. Real-time.

Sentiment

Per-ticker sentiment scores refreshed every 5 minutes. Backtested: 18.1% CAGR on Nasdaq 100 L/S.

04Methodology

How it is built

01
Licensed collection
Bulk export under license from private social-finance apps with 10 to 18 years of history, plus real-time ingestion from public-platform APIs. No web scraping.
02
PII removal
All personally identifiable information is stripped before delivery: user IDs pseudonymized, real names removed, in-text PII redacted. The corpus is PII-free and MNPI-free.
03
Three-layer construction
broad (the cleaned underlying feed with source attribution) becomes curated (strict and balanced bands, the audit bridge) becomes signal (the post-processed symbol-day panel). The wide band is excluded from delivery.
04
Spam and promo filtering
Multi-layer filtering: platform moderation, bot detection on posting frequency, content patterns and account age, and known-spam exclusion. The filter removes roughly 95% of coordinated 2024 promotions.
05
Symbol mapping with provenance
Each message links to securities through a mapping view, and every signal row is traceable back to the exact contributing messages by record_id.
06
Point-in-time aggregation
Only message-time information enters feature construction - no future-return data. The signal panel carries trade_date and trade_lag_days so backtests are look-ahead-free.

05Evals

How we validate

What each evaluation measures and how it is run. Where no benchmark is published, we show the methodology and say so.

NASDAQ-100 long/short

Measures

Whether the daily sentiment score separates winners from losers in a tradeable portfolio.

Method

Long the most-bullish names, short the most-bearish, rebalanced on the score across the NASDAQ-100 universe.

Result

Published backtest: 18.1% CAGR, 1.20 Sharpe. See the research paper for construction and caveats.

Ground truth vs NLP baseline

Measures

Whether the author-stated sentiment label beats an inferred NLP-sentiment feed.

Method

Hold the strategy construction fixed and swap only the sentiment source - human labels vs an NLP feed - over the same period.

Result

Author labels win: 122% vs 95% cumulative long/short return. The label is observed, not modeled.

Spam-filter catch rate

Measures

How much coordinated pump-and-promo content the filter removes.

Method

Evaluated against known 2024 stock-promotion campaigns.

Result

Roughly 95% of coordinated promotions removed.

06Graders

Ground truth

What correct means for this data, and how it is established.

Ground truth

For sentiment, the author own bullish or bearish label at post time - observed, not inferred, so there is zero sentiment-model risk. For signal validation, realized forward returns from the price panel.

How it is established

Every signal row is auditable end to end: security_day_signal back to message_security_view, join on record_id into curated messages, and optionally into the broad feed. Two delivered notebooks walk the full provenance trace. Baseline factors are scored against the price panel.

Agreement

No inter-rater figure applies - by design the sentiment label is the author stated stance, so there is no separate human-rater pass to agree with.

07Application

Retail Sentiment Signals

Identify retail investor conviction shifts before they show up in order flow. Track ticker-level attention as a leading indicator for L/S equity strategies.

Event-Driven Flow Detection

Detect retail pile-ins and narrative shifts around earnings, FDA decisions, and macro events. Real-time firehose enables sub-minute signal generation.

Attention as Alpha

Use engagement metrics (pageviews, watchlist adds, message velocity) as a proxy for retail attention - a proven uncorrelated signal for systematic strategies.

08Environment & integration

How you load it

Delivery

S3 push, REST API, WebSocket firehose, Restricted data room

Formats

Parquet, JSON, CSV

Auth

Licensed for internal research, model development, and portfolio management; redistribution prohibited without a separate agreement. PII-free and MNPI-free public commentary.

Cadence

Real-time streaming at roughly 200ms firehose latency, or daily batch. History is corrected and backfilled weekly.

quickstart.sh

# Quant entry point: the symbol-day signal panel
signal/normalized/security_day_signal.parquet
signal/normalized/security_day_research.parquet
 
# Engineering entry: raw feed + manifest
broad/messages.parquet
delivery_manifest.json
 
# Recommended first tests: 1-day, 5-day, 20-day horizons.
# Audit any signal row back to its source messages by record_id.

09Related research

Retail Financial Sentiment as a Systematic SignalRead →

Request a sample