In the fast-paced world of algorithmic trading, communication between human intent and AI execution often breaks down—especially when emotional, nuanced, or ambiguous language is involved. HAIF (Human-AI Interpreter Framework) is designed to bridge that gap.
HAIF specializes in translating emotionally rich or informal human language into precise, machine-readable logic, enabling smoother and safer collaboration between traders, analysts, and AI systems. Whether it’s interpreting “I’m worried about market volatility” into risk-adjusted trade parameters, or rephrasing vague preferences like “play it safe today,” HAIF helps transform human judgment into structured input for automated systems.
- Emotional Language Parsing: Understands colloquial, emotional, and context-rich language commonly used by human traders.
- AI-Compatible Translation: Converts human expressions into formats suitable for algorithmic interpretation and execution.
- Trading-Aware Vocabulary: Trained with financial language to maintain accuracy in domain-specific contexts.
- Interpretability Layer: Provides transparent, traceable mappings between human inputs and AI actions to support trust and auditability.
- Role-Adaptive Profiles: Adapts its interpretation style based on the user (e.g., analyst, portfolio manager, risk officer).
- Trader-AI Communication: Translate high-level directives like “keep it conservative” or “look for momentum” into precise trading signals.
- Emotion-Aware Trading Bots: Enhance bots with the ability to interpret behavioral cues or psychological states.
- Cross-Team Collaboration: Help bridge language between departments (e.g., strategy vs. execution) by standardizing inputs.
- Education & Onboarding: Assist new traders in understanding how natural language translates to trading strategies and rules.
This diagram illustrates how HAIF transforms emotional human expressions into machine-readable commands for algorithmic trading systems.
We welcome collaborators from the fields of natural language processing, finance, human-computer interaction, and cognitive science. Please open an issue or pull request to contribute ideas or improvements.
This project is licensed under the MIT License. See the LICENSE file for details.
HAIF is grounded in multidisciplinary research across natural language understanding, human-computer collaboration, and emotion-aware AI systems, particularly in financial and decision-support contexts. Foundational references include:
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Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy.
https://doi.org/10.1080/10447318.2020.1741118 -
Amershi, S., et al. (2019). Guidelines for Human-AI Interaction.
https://doi.org/10.1145/3290605.3300233 -
Luger, E., & Sellen, A. (2016). “Like Having a Really Bad PA”: The Gulf between User Expectation and Experience of Conversational Agents.
https://doi.org/10.1145/2858036.2858288 -
Seaborn, K., & Fels, D. I. (2015). Human-Centered AI and the Implications for Design.
https://doi.org/10.1093/iwc/iwu030
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Calefato, F., et al. (2018). Sentiment Analysis for Software Engineering.
https://doi.org/10.1145/3183519.3183540 -
Choi, E., et al. (2020). Detecting Linguistic Characteristics of Emotion-Aware Language in Human-AI Dialogues.
https://doi.org/10.18653/v1/2020.emnlp-main.621 -
Liu, P., et al. (2023). How Do Users Express and AI Understand Emotion in Professional Tasks?
https://doi.org/10.1145/3544548.3581500 -
Binns, R., et al. (2018). ‘It’s Reducing a Human Being to a Percentage’.
https://doi.org/10.1145/3173574.3173951
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Lopez de Prado, M. (2018). Advances in Financial Machine Learning.
ISBN: 978-1119482086 -
Das, S. R. (2014). Text and Context: Language Analytics in Finance.
https://doi.org/10.1561/0500000031 -
Nielsen, M. A. (2019). Neural Networks and Deep Learning.
http://neuralnetworksanddeeplearning.com/
HAIF – Bridging the gap between how humans feel and how AIs compute.