{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"fingpt-agent","slug":"fingpt-agent","name":"FinGPT Agent","type":"agent","url":"https://github.com/AI4Finance-Foundation/FinGPT","page_url":"https://unfragile.ai/fingpt-agent","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"fingpt-agent__cap_0","uri":"capability://code.generation.editing.parameter.efficient.financial.model.fine.tuning.via.lora.adaptation","name":"parameter-efficient financial model fine-tuning via lora adaptation","description":"Implements Low-Rank Adaptation (LoRA) to fine-tune open-source base models (Llama-2, Falcon, MPT, Bloom, ChatGLM2, Qwen) on financial datasets with ~$300 cost per fine-tuning cycle instead of training from scratch. Uses rank-decomposed weight matrices to reduce trainable parameters by 99%+ while maintaining task performance, enabling rapid model updates as new financial data becomes available without full retraining.","intents":["Fine-tune a base LLM on proprietary financial data without prohibitive compute costs","Rapidly update financial models with new market data and earnings transcripts","Customize model behavior for specific trading strategies or risk profiles via RLHF","Deploy financial models on resource-constrained infrastructure"],"best_for":["Quant teams and hedge funds building proprietary financial models","Fintech startups with limited ML infrastructure budgets","Researchers studying domain adaptation in financial NLP"],"limitations":["LoRA rank and alpha hyperparameters require tuning per financial domain (sentiment vs forecasting)","Fine-tuning quality depends on base model selection; smaller models (6-7B) may struggle with complex financial reasoning","No built-in mechanism for continuous online learning; requires batch retraining cycles","Instruction tuning quality varies across financial tasks; sentiment analysis performs better than price forecasting"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+ for GPU acceleration","Hugging Face transformers library 4.30+","Minimum 16GB VRAM for 7B model fine-tuning, 40GB+ for 13B models","Labeled financial dataset (news, earnings calls, reports) for supervised fine-tuning"],"input_types":["text (financial news articles, earnings transcripts, 10-K/10-Q reports)","structured data (stock prices, OHLCV data, financial metrics)","instruction-response pairs for instruction tuning"],"output_types":["fine-tuned model weights (LoRA adapters as .safetensors or .bin files)","model checkpoints compatible with Hugging Face transformers","evaluation metrics (perplexity, task-specific F1/accuracy)"],"categories":["code-generation-editing","financial-ml"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_1","uri":"capability://text.generation.language.multi.source.financial.sentiment.analysis.with.domain.specific.fine.tuning","name":"multi-source financial sentiment analysis with domain-specific fine-tuning","description":"Executes sentiment classification on financial text (news, earnings calls, social media) using FinGPT v3 models fine-tuned on financial corpora with domain-specific vocabulary and sentiment labels (bullish/bearish/neutral). Implements a data engineering pipeline that processes raw financial text through tokenization, entity recognition, and sentiment label extraction, then evaluates against financial sentiment benchmarks to measure domain adaptation quality.","intents":["Extract market sentiment from financial news and earnings call transcripts","Classify investor sentiment from social media and financial forums","Build sentiment-based trading signals for algorithmic strategies","Benchmark sentiment model performance against financial domain baselines"],"best_for":["Algorithmic traders building sentiment-driven strategies","Risk analysts monitoring market sentiment shifts","Financial data providers enriching news feeds with sentiment scores","Researchers evaluating domain-specific NLP model adaptation"],"limitations":["Sentiment labels are binary/ternary (bullish/neutral/bearish); no intensity scoring or mixed sentiment handling","Performance degrades on out-of-domain text (e.g., social media slang vs formal earnings calls)","Benchmark datasets are relatively small (~10K samples); may not capture long-tail financial terminology","No real-time streaming sentiment; requires batch processing of text documents","Sarcasm and financial jargon (e.g., 'dead cat bounce') often misclassified"],"requires":["Python 3.8+","Transformers library 4.30+","Pre-trained FinGPT sentiment model (7B-13B parameters)","GPU with 8GB+ VRAM for inference, 16GB+ for fine-tuning","Financial text corpus for evaluation (news articles, earnings transcripts)"],"input_types":["text (financial news articles, earnings call transcripts, social media posts, analyst reports)","structured metadata (ticker symbols, dates, source attribution)"],"output_types":["sentiment labels (bullish, neutral, bearish)","confidence scores (0-1 probability per class)","structured JSON with sentiment + entity extraction"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_10","uri":"capability://data.processing.analysis.multi.market.financial.analysis.with.localized.data.sources","name":"multi-market financial analysis with localized data sources","description":"Extends financial analysis capabilities to multiple markets (US, Chinese, etc.) by integrating localized data sources, market-specific terminology, and regional financial conventions. The system implements market-specific data pipelines (e.g., Tencent Finance for Chinese stocks) and fine-tunes models on regional financial corpora to handle market-specific language and concepts, enabling cross-market analysis and comparison.","intents":["Analyze Chinese market stocks using localized data sources and terminology","Compare financial metrics across US and Chinese markets","Forecast prices for international stocks with region-specific models","Extract insights from non-English financial documents and earnings calls"],"best_for":["Global investment firms analyzing multiple markets","International robo-advisors serving multi-market portfolios","Researchers studying cross-market financial NLP","Fintech platforms expanding to new geographic markets"],"limitations":["Market-specific models require separate fine-tuning; no single model generalizes across markets","Data availability varies significantly by market; Chinese market data may be less accessible than US data","Financial terminology and conventions differ by market; direct translation may lose meaning (e.g., 'guidance' concept doesn't exist in some markets)","Regulatory differences affect financial metrics and reporting; direct comparison across markets is challenging","Model performance may degrade when applied to new markets without retraining"],"requires":["Python 3.8+","Market-specific data APIs (Alpha Vantage for US, Tencent Finance for China, etc.)","Localized financial corpora for fine-tuning (earnings transcripts, news in local language)","Language models supporting non-English text (e.g., ChatGLM2 for Chinese)","GPU with 16GB+ VRAM for multi-market model inference"],"input_types":["market-specific financial data (prices, fundamentals, news)","localized financial documents (earnings calls in local language)","regional economic indicators","market-specific metadata (trading hours, holidays, regulations)"],"output_types":["market-specific analysis and recommendations","cross-market comparison reports","localized sentiment and forecast signals","market-specific risk metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_11","uri":"capability://text.generation.language.multi.language.financial.analysis.with.domain.adaptation","name":"multi-language financial analysis with domain adaptation","description":"Extends financial analysis capabilities to non-English markets (particularly Chinese markets) through language-specific fine-tuning and domain adaptation. Handles language-specific financial terminology, reporting standards (annual vs quarterly), and regulatory environments through separate model checkpoints and preprocessing pipelines tailored to each language and market. Enables forecasting and sentiment analysis on Chinese stocks and financial documents with models trained on Chinese financial corpora.","intents":["Analyze Chinese financial news and earnings reports for sentiment and signals","Forecast Chinese stock prices using localized financial data and reporting standards","Extract entities and relationships from Chinese financial documents","Build multi-market trading systems covering both US and Chinese equities"],"best_for":["Global hedge funds with exposure to Chinese markets","Researchers studying cross-market financial NLP","Trading systems covering multiple geographic markets","Organizations expanding from US to Chinese financial markets"],"limitations":["Chinese financial data is less readily available than US data; requires partnerships with Chinese data providers or web scraping","Chinese reporting standards differ from US (annual vs quarterly, different metrics); models require separate training","Chinese financial terminology and abbreviations are market-specific; generic Chinese NLP models don't understand financial context","Regulatory environment differs significantly (capital controls, disclosure requirements); models trained on US data don't transfer to Chinese markets"],"requires":["Chinese language base model (ChatGLM2, Qwen, or Chinese-capable Llama variant)","Chinese financial training data (news articles, earnings reports, stock prices)","Chinese financial domain vocabulary and entity lists","Chinese NLP preprocessing tools (tokenization, entity recognition)","Chinese financial data provider (Tencent, Sina, or web scraping)","Separate model checkpoints for Chinese vs English markets"],"input_types":["Chinese text (financial news, earnings reports, analyst reports)","Chinese market data (stock prices, volume, fundamentals)","Chinese metadata (company names, tickers, reporting dates)"],"output_types":["Chinese sentiment analysis (bullish/bearish/neutral)","Chinese stock price forecasts","extracted entities and relationships from Chinese documents","Chinese financial metrics and analysis"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_2","uri":"capability://planning.reasoning.stock.price.forecasting.via.temporal.sequence.modeling.with.financial.context","name":"stock price forecasting via temporal sequence modeling with financial context","description":"Predicts future stock price movements by combining historical OHLCV data with financial context (earnings announcements, news sentiment, macroeconomic indicators) through a sequence-to-sequence architecture. The FinGPT Forecaster layer processes time-series data through a data pipeline that aligns temporal events (earnings dates, news publication) with price data, then uses fine-tuned LLMs to generate price predictions with confidence intervals, supporting both univariate (single stock) and multivariate (sector/market) forecasting.","intents":["Generate 1-day to 30-day ahead stock price predictions for trading signals","Forecast Chinese market stocks with localized financial data sources","Combine technical indicators with fundamental analysis for hybrid predictions","Evaluate forecast accuracy against historical price data"],"best_for":["Quantitative traders building multi-factor prediction models","Robo-advisors generating price targets for portfolio recommendations","Risk managers forecasting portfolio volatility","Researchers studying LLM-based time-series forecasting"],"limitations":["Forecasting accuracy degrades significantly beyond 5-day horizon due to market noise and unpredictable events","Requires aligned historical data (price + news + earnings dates); missing data points reduce prediction quality","No built-in handling of market regime changes (bull/bear transitions) or black swan events","Chinese market forecasting relies on specific data sources (e.g., Tencent Finance); may not generalize to other markets","Confidence intervals are model-generated, not calibrated to actual forecast error distributions"],"requires":["Python 3.8+","Time-series data (OHLCV prices at daily/hourly granularity)","Financial event data (earnings dates, news timestamps, economic calendar)","Pre-trained FinGPT Forecaster model (7B-13B parameters)","GPU with 16GB+ VRAM for inference on large portfolios","Historical price data for backtesting (minimum 2-5 years)"],"input_types":["structured time-series data (OHLCV prices, volume, adjusted close)","financial events (earnings announcement dates, dividend dates)","text (news articles, earnings call transcripts, analyst reports)","macroeconomic indicators (interest rates, inflation, GDP growth)"],"output_types":["price predictions (point estimates + confidence intervals)","direction predictions (up/down/neutral)","volatility forecasts","structured JSON with prediction metadata (model version, data sources used)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_3","uri":"capability://memory.knowledge.financial.report.analysis.via.raptor.hierarchical.rag.system","name":"financial report analysis via raptor hierarchical rag system","description":"Analyzes long-form financial documents (10-K, 10-Q, earnings transcripts) using a RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) RAG system that recursively summarizes document sections into a tree hierarchy, enabling multi-level retrieval and reasoning. The system chunks financial reports, embeds chunks into a vector database, then retrieves relevant sections at multiple abstraction levels (raw text → summary → abstract) to answer complex financial questions requiring cross-document reasoning.","intents":["Extract key financial metrics and risk factors from 10-K/10-Q filings","Answer complex questions spanning multiple sections of earnings transcripts","Compare financial performance across multiple companies' reports","Generate investment theses based on fundamental analysis of filings"],"best_for":["Equity research analysts automating fundamental analysis workflows","Investment managers synthesizing insights from hundreds of earnings calls","Compliance teams extracting risk disclosures from regulatory filings","Financial data providers building document-based search products"],"limitations":["RAPTOR tree construction requires careful tuning of chunk size and summarization depth; suboptimal settings degrade retrieval quality","Recursive summarization introduces compounding errors; key details may be lost in higher-level abstracts","Vector embeddings may miss domain-specific financial relationships (e.g., 'debt-to-equity ratio' vs 'leverage')","Requires external vector database (Pinecone, Weaviate, Chroma); no built-in persistence","Processing time scales with document length; 100+ page 10-K filings may require 5-10 minutes for tree construction"],"requires":["Python 3.8+","Transformers library 4.30+ for embeddings","Vector database (Pinecone, Weaviate, Chroma, or Milvus)","Embedding model (e.g., sentence-transformers/all-MiniLM-L6-v2 or financial-specific embeddings)","GPU with 8GB+ VRAM for embedding generation (optional but recommended)","Financial documents in text format (PDF extraction required for native PDFs)"],"input_types":["text (10-K/10-Q filings, earnings call transcripts, annual reports)","structured metadata (company ticker, filing date, document type)","natural language questions (e.g., 'What are the main risk factors?')"],"output_types":["retrieved document excerpts with relevance scores","multi-level summaries (raw text → section summary → document abstract)","structured answers to financial questions","citation metadata (source document, page number, section)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_4","uri":"capability://search.retrieval.multi.source.financial.data.retrieval.with.news.context.enhancement","name":"multi-source financial data retrieval with news context enhancement","description":"Retrieves relevant financial information from heterogeneous sources (news articles, stock prices, earnings transcripts, macroeconomic data) and augments retrieval results with contextual news articles to improve answer quality. The system implements a multi-source retrieval pipeline that queries different data sources in parallel, ranks results by relevance to financial queries, and enriches retrieved data with recent news context to provide up-to-date market perspective.","intents":["Retrieve historical stock prices and fundamental metrics for a given ticker","Find relevant news articles and earnings transcripts for a company","Augment price data with sentiment and news context for analysis","Build comprehensive financial profiles combining multiple data modalities"],"best_for":["Financial data aggregators building unified search interfaces","Robo-advisors enriching investment recommendations with news context","Research platforms automating data collection for fundamental analysis","News-driven trading systems combining price data with sentiment"],"limitations":["Multi-source retrieval introduces latency; parallel queries may timeout if any source is slow","News context enhancement requires real-time news feeds; historical news may be incomplete or unavailable","Relevance ranking across heterogeneous sources (prices vs text) requires domain-specific scoring functions","No built-in deduplication; same news story from multiple sources may appear multiple times","Data freshness varies by source; stock prices may be delayed 15+ minutes on free APIs"],"requires":["Python 3.8+","API keys for financial data sources (Alpha Vantage, IEX Cloud, Finnhub, or similar)","News API access (NewsAPI, Finnhub, or proprietary news feeds)","Vector database for semantic search over news articles","Embedding model for ranking relevance across sources"],"input_types":["ticker symbols or company names","natural language financial queries","date ranges for historical data retrieval","structured filters (sector, market cap, etc.)"],"output_types":["structured financial data (prices, metrics, fundamentals)","ranked news articles with relevance scores","earnings transcripts and call summaries","unified JSON response combining multiple data modalities"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_5","uri":"capability://data.processing.analysis.financial.nlp.task.benchmarking.and.evaluation.framework","name":"financial nlp task benchmarking and evaluation framework","description":"Provides standardized benchmark datasets and evaluation metrics for assessing FinGPT model performance on core financial NLP tasks (sentiment analysis, price forecasting, named entity recognition, relation extraction). The framework implements task-specific evaluation protocols (e.g., F1 score for sentiment, RMSE for price forecasting) and compares model outputs against gold-standard annotations, enabling quantitative assessment of domain adaptation quality and model selection.","intents":["Evaluate fine-tuned financial models against standardized benchmarks","Compare performance of different base models (Llama-2 vs Falcon vs MPT) on financial tasks","Track model performance improvements across fine-tuning iterations","Publish reproducible results for financial NLP research"],"best_for":["Researchers developing financial NLP models","ML engineers selecting base models for production deployment","Teams evaluating custom fine-tuning approaches","Academic groups publishing financial AI benchmarks"],"limitations":["Benchmark datasets are relatively small (~10K samples per task); may not reflect production data distribution","Evaluation metrics are task-specific; no unified scoring across different financial tasks","Benchmarks are static; do not capture evolving financial terminology or market regimes","No built-in statistical significance testing; difficult to determine if performance differences are meaningful","Benchmark data may contain annotation errors or inconsistencies from crowdsourcing"],"requires":["Python 3.8+","Benchmark datasets (provided in repository or custom datasets)","Evaluation libraries (scikit-learn for metrics, seqeval for NER)","Pre-trained models to evaluate","GPU optional but recommended for large-scale evaluation"],"input_types":["model predictions (text, labels, scores)","gold-standard annotations (human-labeled financial data)","task specifications (sentiment, forecasting, NER, relation extraction)"],"output_types":["task-specific metrics (F1, accuracy, RMSE, MAE)","confusion matrices and error analysis","benchmark leaderboards and comparison tables","detailed evaluation reports with per-sample analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_6","uri":"capability://planning.reasoning.robo.advising.with.personalized.financial.recommendations","name":"robo-advising with personalized financial recommendations","description":"Generates personalized investment recommendations by combining sentiment analysis, price forecasting, and fundamental analysis through a decision-making pipeline that ranks assets based on multiple factors (expected return, risk, sentiment, fundamentals). The system implements a portfolio optimization layer that balances recommendations across asset classes and risk profiles, then generates natural language explanations for each recommendation to support user decision-making.","intents":["Generate personalized stock recommendations based on user risk profile","Explain investment recommendations in natural language","Rebalance portfolios based on updated sentiment and forecast signals","Provide alternative recommendations for different risk tolerances"],"best_for":["Fintech platforms building robo-advisor features","Wealth management firms automating recommendation workflows","Individual investors seeking AI-assisted portfolio guidance","Financial advisors augmenting their recommendations with AI"],"limitations":["Recommendations are based on historical patterns; may fail during market regime changes or black swan events","No built-in risk management; recommendations may concentrate risk in correlated assets","Natural language explanations may be verbose or contain financial jargon unfamiliar to retail investors","Personalization requires user profile data (risk tolerance, investment horizon, constraints); cold-start problem for new users","No regulatory compliance features (suitability analysis, disclosure requirements); requires additional compliance layer for production"],"requires":["Python 3.8+","Fine-tuned FinGPT sentiment and forecasting models","User profile data (risk tolerance, investment constraints, portfolio holdings)","Fundamental data (P/E ratios, dividend yields, debt levels)","Portfolio optimization library (e.g., cvxpy, scipy.optimize)"],"input_types":["user profile (risk tolerance, investment horizon, constraints)","portfolio holdings (current positions, weights)","market data (prices, fundamentals, sentiment scores)","user preferences (sector preferences, ESG criteria)"],"output_types":["ranked investment recommendations with scores","natural language explanations for each recommendation","portfolio rebalancing suggestions","risk metrics (expected return, volatility, Sharpe ratio)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_7","uri":"capability://automation.workflow.continuous.financial.data.pipeline.with.real.time.nlp.processing","name":"continuous financial data pipeline with real-time nlp processing","description":"Implements a real-time data engineering pipeline that continuously ingests financial data (news, prices, earnings transcripts) and applies NLP processing (tokenization, entity recognition, sentiment analysis) to extract signals for downstream tasks. The pipeline handles high temporal sensitivity and low signal-to-noise ratio in financial data through filtering, deduplication, and quality checks, enabling rapid incorporation of new financial information into model predictions.","intents":["Ingest and process financial news in real-time for sentiment-based trading","Build continuously updated training datasets for model fine-tuning","Monitor data quality and detect anomalies in financial feeds","Synchronize multiple data sources (prices, news, fundamentals) by timestamp"],"best_for":["Algorithmic trading firms requiring real-time data processing","Financial data providers building streaming pipelines","ML teams automating continuous model retraining workflows","Risk monitoring systems detecting market anomalies"],"limitations":["Real-time processing introduces latency; NLP inference may lag behind market-moving events by seconds to minutes","High-frequency data streams require careful resource management; processing all news/tweets may exceed compute budgets","Data quality issues (duplicate news, spam, misinformation) require sophisticated filtering; simple deduplication may miss variations","Temporal synchronization across sources is challenging; prices and news may have different timestamps and granularities","No built-in handling of data source failures or outages; requires fallback mechanisms"],"requires":["Python 3.8+","Message queue (Kafka, RabbitMQ) for streaming data ingestion","Data processing framework (Apache Spark, Flink, or Python asyncio)","Financial data APIs with streaming support (WebSocket connections)","NLP models for real-time inference (quantized or distilled models for latency)","Time-series database (InfluxDB, TimescaleDB) for storing processed signals"],"input_types":["streaming financial news (RSS feeds, news APIs, social media)","real-time price data (WebSocket feeds from exchanges)","earnings call transcripts (batch or streaming)","macroeconomic data releases (scheduled events)"],"output_types":["processed signals (sentiment scores, entity mentions, price predictions)","quality metrics (data freshness, completeness, anomaly flags)","structured events (earnings announcement, price spike, sentiment shift)","time-series data ready for model training or trading systems"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_8","uri":"capability://data.processing.analysis.named.entity.recognition.and.relation.extraction.for.financial.documents","name":"named entity recognition and relation extraction for financial documents","description":"Identifies and extracts financial entities (companies, people, financial instruments, metrics) and relationships between them (e.g., 'Company X acquired Company Y', 'CEO John Smith resigned') from unstructured financial text using fine-tuned sequence labeling models. The system uses token-level classification to tag entities and relation extraction models to identify connections, enabling structured knowledge extraction from earnings calls, news articles, and reports.","intents":["Extract company names, executives, and financial metrics from earnings transcripts","Identify M&A transactions and corporate events from news articles","Build knowledge graphs of financial relationships and transactions","Populate structured databases from unstructured financial documents"],"best_for":["Financial data providers building structured databases from news","Knowledge graph builders for financial intelligence","Compliance teams tracking executive changes and corporate events","Research teams analyzing financial networks and relationships"],"limitations":["Entity recognition performance degrades on rare entities (e.g., small-cap companies, new executives) due to limited training data","Relation extraction is task-specific; models trained for M&A may not generalize to other relation types","Nested or overlapping entities (e.g., 'Apple Inc.' vs 'Apple') require careful handling; simple token-level classification may fail","Financial terminology is domain-specific; general NER models perform poorly on financial documents","Coreference resolution (e.g., 'the company' referring to Apple) is not handled; requires separate module"],"requires":["Python 3.8+","Transformers library 4.30+ with token classification support","Annotated financial NER/RE datasets for fine-tuning","Pre-trained financial NER/RE models (or base models for fine-tuning)","GPU with 8GB+ VRAM for inference"],"input_types":["text (earnings call transcripts, news articles, financial reports)","entity type specifications (company, person, financial instrument, metric)","relation type specifications (acquisition, resignation, partnership, etc.)"],"output_types":["entity spans with types and confidence scores","relation tuples (entity1, relation_type, entity2)","structured JSON with entities and relations","knowledge graph edges for downstream processing"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__cap_9","uri":"capability://code.generation.editing.instruction.tuning.for.financial.task.customization","name":"instruction tuning for financial task customization","description":"Enables customization of fine-tuned financial models through instruction tuning, where models learn to follow natural language instructions for specific financial tasks. The system uses instruction-response pairs (e.g., 'Analyze the sentiment of this earnings call' → sentiment label) to teach models task-specific behavior, allowing users to define custom financial tasks without retraining from scratch. Supports reinforcement learning from human feedback (RLHF) for further personalization.","intents":["Customize model behavior for domain-specific financial tasks","Define new financial analysis tasks through natural language instructions","Fine-tune models on proprietary financial workflows and terminology","Improve model alignment with user preferences through RLHF"],"best_for":["Financial institutions with proprietary analysis workflows","Fintech teams building custom financial AI features","Researchers studying instruction-following in financial domains","Teams requiring models to follow domain-specific conventions"],"limitations":["Instruction tuning quality depends on quality and diversity of instruction-response pairs; small datasets may overfit","RLHF requires human feedback at scale; expensive and time-consuming for large-scale customization","Instruction following may conflict with base model training; models may 'forget' general financial knowledge","No built-in mechanism for instruction versioning or A/B testing; difficult to track which instructions improve performance","Instruction tuning adds training overhead (~$300-500 per task) on top of base fine-tuning cost"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+","Instruction-response dataset (minimum 500-1000 examples per task)","Pre-trained financial base model","GPU with 16GB+ VRAM for instruction tuning","Optional: RLHF infrastructure (reward model, human feedback collection)"],"input_types":["instruction-response pairs (natural language instructions + expected outputs)","financial task specifications","human feedback for RLHF (preference pairs or reward scores)"],"output_types":["instruction-tuned model weights","task-specific model checkpoints","evaluation metrics on instruction-following tasks","RLHF reward model (if using RLHF)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fingpt-agent__headline","uri":"capability://data.processing.analysis.open.source.financial.ai.agent","name":"open-source financial ai agent","description":"FinGPT is an open-source financial AI agent that specializes in sentiment analysis, robo-advising, and quantitative trading signals, tailored for financial applications.","intents":["best financial AI agent","financial AI for trading","open-source robo-advising tools","financial sentiment analysis software","AI for quantitative trading signals"],"best_for":["financial analysts","traders","investment firms"],"limitations":["requires financial data sources","may need fine-tuning for specific tasks"],"requires":["access to financial data","basic understanding of AI"],"input_types":["financial news","market data","financial reports"],"output_types":["trading signals","sentiment scores","financial insights"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+ for GPU acceleration","Hugging Face transformers library 4.30+","Minimum 16GB VRAM for 7B model fine-tuning, 40GB+ for 13B models","Labeled financial dataset (news, earnings calls, reports) for supervised fine-tuning","Transformers library 4.30+","Pre-trained FinGPT sentiment model (7B-13B parameters)","GPU with 8GB+ VRAM for inference, 16GB+ for fine-tuning","Financial text corpus for evaluation (news articles, earnings transcripts)","Market-specific data APIs (Alpha Vantage for US, Tencent Finance for China, etc.)"],"failure_modes":["LoRA rank and alpha hyperparameters require tuning per financial domain (sentiment vs forecasting)","Fine-tuning quality depends on base model selection; smaller models (6-7B) may struggle with complex financial reasoning","No built-in mechanism for continuous online learning; requires batch retraining cycles","Instruction tuning quality varies across financial tasks; sentiment analysis performs better than price forecasting","Sentiment labels are binary/ternary (bullish/neutral/bearish); no intensity scoring or mixed sentiment handling","Performance degrades on out-of-domain text (e.g., social media slang vs formal earnings calls)","Benchmark datasets are relatively small (~10K samples); may not capture long-tail financial terminology","No real-time streaming sentiment; requires batch processing of text documents","Sarcasm and financial jargon (e.g., 'dead cat bounce') often misclassified","Market-specific models require separate fine-tuning; no single model generalizes across markets","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=fingpt-agent","compare_url":"https://unfragile.ai/compare?artifact=fingpt-agent"}},"signature":"Wf4PUUf/kdZjadjaR9xVaLu5pnqSrS7LEdON37aovY98OEPW+BA0Spec1I/Gl/4+UsxMKarBk0/RM57EVETWCw==","signedAt":"2026-06-23T13:11:09.440Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fingpt-agent","artifact":"https://unfragile.ai/fingpt-agent","verify":"https://unfragile.ai/api/v1/verify?slug=fingpt-agent","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}