SENSE-ACT

Sentiment arbitrage engine for oil markets.
Shadow mode — no real money, real architecture.

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A Ras Tanura pipeline engineer with 200 followers posts something on Twitter. Oil futures move 3 minutes later. Reuters publishes the same story 8 minutes after that. By then it's already priced in.

The signal was never in the follower count. It was in the information type.

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What It Does

Shadow-mode sentiment arbitrage engine tuned for oil/energy. Replaces standard follower-count weighting with explicit domain expertise scoring. The architecture is production-grade. The execution is virtual.

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Signal Flow

RSS/News API -> FinBERT scoring -> Semantic dedup (cosine) -> Welford z-score
-> Half-life decay -> Influence weighting -> Kill-switch check
-> Monte Carlo slippage -> Shadow book

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Key Components

Component	What It Does
FinBERT	ProsusAI/finbert for financial text sentiment. Handles "maintains output despite pressure" correctly.
Semantic Dedup	Catches Reuters headline + Bloomberg paraphrase. Cosine similarity, threshold 0.82.
Welford z-score	Single-pass variance, O(1) storage. Flags anomalies in real-time.
Half-life decay	Signals lose 50% weight every 120s. Consistent with Hasbrouck (1991).
Kill-switch	Halts if spread or VIX doubles in 60 seconds.
Monte Carlo slippage	Realistic execution cost modeling.

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Influence Weighting

weight = log10(followers) * hub_boost * domain_expertise * accuracy

A hub account with domain expertise gets 2.5x boost. A broadcast megaphone with 800K followers that just reposts Reuters 6 minutes late isn't worth much.

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Quick Start

pip install -r requirements.txt

# Run the backtest
python backtest.py

# Launch the dashboard
python dashboard.py

# Start the signal processor
python orchestrator.py

Tests

python run_tests.py
# 30/30 passing

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License

MIT.