{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-onepunchmonk--agentquant","slug":"onepunchmonk--agentquant","name":"AgentQuant","type":"agent","url":"https://github.com/OnePunchMonk/AgentQuant","page_url":"https://unfragile.ai/onepunchmonk--agentquant","categories":["ai-agents"],"tags":["agentic-ai","ai-for-trading","ai-trading","ai-trading-agent","algorithmic-trading","autonomous-agent","finance-ai","fintech","langchain","langgraph","portfolio-optimization","quant-agent","quantitative-finance","quantitative-research","quantitative-trading","trading-agent","trading-bot","trading-strategies","vectorbt","yfinance"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-onepunchmonk--agentquant__cap_0","uri":"capability://planning.reasoning.autonomous.strategy.generation.from.stock.universe","name":"autonomous-strategy-generation-from-stock-universe","description":"Transforms a list of stock symbols into mathematically formulated trading strategies through an agentic LLM workflow orchestrated by LangChain + LangGraph. The system chains feature engineering outputs and market regime classification into Gemini Pro prompts that generate strategy logic, which is then backtested and visualized without requiring manual coding or domain expertise from the user.","intents":["I want to input a stock list and get a complete, backtested trading strategy without writing code","I need to generate multiple strategy variations automatically from market data","I want to understand what trading logic the AI generated and why it works"],"best_for":["non-technical traders and portfolio managers seeking rapid strategy prototyping","quantitative researchers wanting to accelerate the research cycle from weeks to minutes","fintech teams building automated trading research platforms"],"limitations":["Gemini Pro API dependency introduces latency (1-2 minutes per strategy generation) and rate limiting constraints","Generated strategies are mathematical formulations only — no live trading execution or real-time rebalancing","Strategy quality depends entirely on feature engineering and regime detection accuracy; garbage-in-garbage-out from poor market data","No multi-asset class support — limited to equities with yfinance data availability"],"requires":["Python 3.9+","Google Gemini Pro API key with quota for LLM calls","Stock symbols available in yfinance (US equities primarily)","Streamlit 1.0+ for dashboard visualization"],"input_types":["stock symbols (list of tickers)","configuration parameters (date ranges, risk thresholds, regime definitions)"],"output_types":["strategy formulations (mathematical logic as text)","backtest performance metrics (Sharpe ratio, max drawdown, win rate)","interactive Streamlit dashboard with charts and statistics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_1","uri":"capability://data.processing.analysis.multi.indicator.feature.engineering.pipeline","name":"multi-indicator-feature-engineering-pipeline","description":"Automatically computes 50+ technical indicators (momentum, volatility, trend, mean-reversion) from raw OHLCV data using pandas and numpy, organizing them into a structured feature matrix that feeds downstream regime detection and strategy generation. The engine normalizes and validates all indicators to ensure numerical stability for LLM consumption and backtesting calculations.","intents":["I need to extract technical features from price data without manually coding each indicator","I want a consistent, validated feature matrix that works with both AI agents and backtesting engines","I need to ensure feature quality and numerical stability across different market conditions"],"best_for":["traders building feature-rich strategy pipelines without indicator library expertise","AI/ML engineers needing structured financial feature inputs for LLM agents","quantitative teams standardizing feature engineering across multiple strategies"],"limitations":["Fixed set of 50+ indicators — no dynamic indicator selection or custom indicator support","Indicator calculations assume sufficient historical data (typically 200+ bars); sparse data produces NaN values","No forward-fill or interpolation for missing data — requires clean OHLCV inputs","Feature matrix grows linearly with indicator count, increasing memory usage for large universes"],"requires":["Python 3.9+","pandas 1.3+, numpy 1.20+","OHLCV data with at least 200 historical bars per symbol","yfinance or compatible data source"],"input_types":["OHLCV dataframes (open, high, low, close, volume)","date ranges and resampling frequencies"],"output_types":["feature matrix (pandas DataFrame with 50+ indicator columns)","normalized numerical arrays for LLM and backtesting consumption"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_10","uri":"capability://automation.workflow.end.to.end.no.code.quantitative.research.automation","name":"end-to-end-no-code-quantitative-research-automation","description":"Abstracts the entire quantitative research workflow (data ingestion, feature engineering, regime detection, strategy generation, backtesting, visualization) into a single end-to-end pipeline that requires only stock symbols and configuration parameters as input, producing complete backtested strategies with professional visualizations. This capability eliminates the traditional weeks-to-months research cycle by automating all intermediate steps and decision-making.","intents":["I want to go from stock list to backtested strategy in minutes without writing any code","I need to rapidly prototype and test multiple strategy ideas across different universes","I want to democratize quantitative research so non-technical users can generate strategies"],"best_for":["non-technical traders and portfolio managers seeking rapid strategy development","fintech teams building automated research platforms for clients","quantitative researchers wanting to accelerate the research cycle"],"limitations":["End-to-end automation sacrifices customization — users cannot easily modify individual pipeline stages or inject custom logic","No live trading execution — strategies are research artifacts only, requiring manual implementation for live trading","Strategy quality depends on all pipeline components working correctly — failures in any stage (data quality, feature engineering, LLM reasoning) cascade through the entire system","Total processing time is 3-6 minutes per universe — not suitable for intraday or high-frequency strategy development"],"requires":["Python 3.9+","All dependencies (LangChain, vectorbt, yfinance, Streamlit, Gemini Pro API key, FRED API key)","Stock symbols available in yfinance","Internet connectivity for API calls"],"input_types":["stock universe (list of ticker symbols)","configuration parameters (date ranges, risk thresholds, regime definitions)","optional: custom strategy constraints or parameter bounds"],"output_types":["complete trading strategies with mathematical formulations","backtest performance metrics and equity curves","interactive Streamlit dashboard with visualizations","exportable reports and strategy documentation"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_2","uri":"capability://data.processing.analysis.market.regime.classification.with.economic.indicators","name":"market-regime-classification-with-economic-indicators","description":"Classifies market conditions into Bull, Bear, or Sideways regimes by analyzing technical features (price momentum, volatility) and macroeconomic indicators (interest rates, inflation from FRED API) using rule-based logic and statistical thresholds. This regime classification is fed into strategy generation to ensure strategies are adapted to current market conditions rather than using one-size-fits-all logic.","intents":["I want to automatically detect whether the market is bullish, bearish, or sideways to adapt strategy parameters","I need to incorporate macroeconomic context (rates, inflation) into trading decisions without manual analysis","I want regime-aware strategies that perform differently based on market conditions"],"best_for":["systematic traders building regime-adaptive strategies","portfolio managers needing macro-aware tactical allocation","AI agents requiring market context for intelligent strategy generation"],"limitations":["Regime classification is rule-based with fixed thresholds — no machine learning adaptation to changing market dynamics","FRED API dependency adds latency (10 seconds) and requires internet connectivity; no offline mode","Regime definitions are static (Bull/Bear/Sideways) — no support for custom regime types or multi-regime hierarchies","Lagged economic data (FRED updates monthly) may not reflect real-time market shifts"],"requires":["Python 3.9+","FRED API key (free from Federal Reserve Economic Data)","Feature matrix from feature engineering pipeline","Historical price data (minimum 252 trading days for annual volatility calculation)"],"input_types":["feature matrix with technical indicators","OHLCV price data","configuration thresholds for regime boundaries"],"output_types":["regime classification (Bull, Bear, Sideways as categorical labels)","regime confidence scores or probability distributions","macroeconomic context metadata (current rates, inflation)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_3","uri":"capability://data.processing.analysis.vectorbt.powered.backtesting.with.performance.metrics","name":"vectorbt-powered-backtesting-with-performance-metrics","description":"Executes high-performance backtests of generated trading strategies using the vectorbt library, which applies strategies to historical OHLCV data and computes comprehensive performance metrics (Sharpe ratio, max drawdown, win rate, cumulative returns) in vectorized operations. The backtesting engine validates strategy logic before presentation and provides detailed performance attribution for strategy evaluation.","intents":["I need to validate that a generated strategy actually works on historical data","I want comprehensive performance metrics (Sharpe, drawdown, returns) to compare strategies","I need fast backtesting (seconds, not hours) to iterate on multiple strategy variations"],"best_for":["quantitative traders validating strategy ideas before live trading","AI agents needing rapid feedback loops for strategy iteration","portfolio managers comparing strategy performance across universes"],"limitations":["Backtesting assumes perfect execution with no slippage, commissions, or market impact — results are optimistic vs real trading","Historical backtest performance does not guarantee future results; overfitting risk is high with automated strategy generation","No transaction cost modeling or realistic order execution simulation — ignores liquidity constraints","Vectorbt is memory-intensive for large universes (1000+ symbols) — may require optimization or batching"],"requires":["Python 3.9+","vectorbt 0.24+","Clean OHLCV data with no gaps or missing values","Strategy formulations with clear entry/exit logic"],"input_types":["OHLCV dataframes","strategy logic (entry/exit signals as boolean arrays or price conditions)","position sizing and risk parameters"],"output_types":["performance metrics (Sharpe ratio, max drawdown, win rate, cumulative returns, Calmar ratio)","equity curves and drawdown charts","trade-by-trade analysis (entry/exit prices, P&L per trade)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_4","uri":"capability://data.processing.analysis.yfinance.and.fred.data.ingestion.pipeline","name":"yfinance-and-fred-data-ingestion-pipeline","description":"Automatically fetches OHLCV market data from yfinance and macroeconomic indicators from FRED API, validates data quality (checks for gaps, outliers, missing values), and normalizes it into pandas DataFrames for downstream processing. The ingestion layer abstracts data source complexity and ensures consistent data formats across the entire pipeline.","intents":["I want to pull market data for a stock list without writing API calls or data validation code","I need both price data and macroeconomic context (rates, inflation) in a unified pipeline","I want to ensure data quality and handle missing values automatically"],"best_for":["traders and researchers needing quick data access without infrastructure setup","AI agents requiring reliable data inputs for strategy generation","teams building trading platforms that need standardized data ingestion"],"limitations":["yfinance is unofficial Yahoo Finance API — no SLA, rate limiting, or guaranteed uptime; data quality varies","FRED API has monthly update lag for economic indicators — not suitable for intraday or high-frequency strategies","No support for alternative data sources (crypto, forex, commodities) — limited to US equities and macro indicators","Data validation is basic (gap detection, outlier flagging) — does not handle corporate actions (splits, dividends) or survivorship bias"],"requires":["Python 3.9+","yfinance 0.2.0+","FRED API key (free registration at Federal Reserve)","Internet connectivity for API calls","Stock symbols available in yfinance (primarily US equities)"],"input_types":["stock ticker symbols (list of strings)","date ranges (start_date, end_date)","data frequency (daily, weekly, monthly)"],"output_types":["OHLCV dataframes (pandas DataFrames with open, high, low, close, volume columns)","macroeconomic indicator series (interest rates, inflation, unemployment)","data quality reports (missing values, outliers, gaps)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_5","uri":"capability://planning.reasoning.langchain.langgraph.agentic.orchestration","name":"langchain-langgraph-agentic-orchestration","description":"Orchestrates the entire quantitative research pipeline using LangChain and LangGraph, implementing a directed acyclic graph (DAG) of processing stages where each node represents a pipeline step (data ingestion, feature engineering, regime detection, strategy generation, backtesting) and edges define data dependencies. The agentic framework enables autonomous decision-making, error recovery, and iterative refinement without manual intervention.","intents":["I want to automate the entire research pipeline from stock symbols to backtested strategies in one execution","I need a system that can handle failures gracefully and retry failed stages","I want the AI to make decisions about strategy parameters and iterations autonomously"],"best_for":["teams building autonomous trading research platforms","AI engineers implementing multi-step financial workflows","organizations wanting to eliminate manual quantitative research bottlenecks"],"limitations":["LangGraph DAG execution is sequential — no parallelization across independent stages, limiting throughput for large universes","Agentic decision-making depends on LLM quality and prompt engineering — poor prompts lead to suboptimal strategy generation","No built-in state persistence — pipeline restarts from beginning if interrupted; requires external database for checkpointing","Debugging multi-stage agentic workflows is complex — error propagation and root cause analysis require detailed logging"],"requires":["Python 3.9+","LangChain 0.1.0+","LangGraph (part of LangChain ecosystem)","Google Gemini Pro API key","All upstream dependencies (yfinance, vectorbt, pandas, etc.)"],"input_types":["stock universe configuration (list of symbols, date ranges, parameters)","pipeline stage definitions (which stages to execute, in what order)"],"output_types":["complete strategy formulations with backtest results","execution logs and stage-by-stage outputs","final Streamlit dashboard with visualizations"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_6","uri":"capability://text.generation.language.gemini.pro.llm.driven.strategy.formulation","name":"gemini-pro-llm-driven-strategy-formulation","description":"Uses Google Gemini Pro LLM to generate trading strategy logic by consuming structured inputs (feature matrix, regime classification, historical performance patterns) and producing human-readable mathematical formulations that define entry/exit conditions, position sizing, and risk management rules. The LLM acts as a creative strategist that synthesizes technical analysis and market context into coherent trading logic.","intents":["I want an AI to generate novel trading strategies that I can understand and audit","I need strategies that adapt to different market regimes (bull, bear, sideways)","I want to leverage LLM reasoning to combine multiple indicators into coherent strategy logic"],"best_for":["traders wanting AI-assisted strategy ideation without manual coding","quantitative teams accelerating strategy research cycles","non-technical users seeking to understand AI-generated trading logic"],"limitations":["Gemini Pro API costs scale with token usage — large universes or frequent regenerations increase costs","LLM-generated strategies may be mathematically unsound or overfitted to historical data — requires backtesting validation","Strategy quality depends heavily on prompt engineering and input feature quality — garbage inputs produce garbage strategies","No guarantee of strategy diversity — LLM may generate similar strategies across different stock universes"],"requires":["Python 3.9+","Google Gemini Pro API key with sufficient quota","LangChain 0.1.0+ for LLM integration","Structured feature matrix and regime classification from upstream pipeline"],"input_types":["feature matrix (50+ technical indicators as numerical arrays)","regime classification (Bull/Bear/Sideways labels)","historical performance patterns (correlation matrices, volatility regimes)","strategy generation prompts (templates defining strategy constraints)"],"output_types":["strategy formulations (text descriptions of entry/exit logic)","mathematical expressions (e.g., 'Buy when RSI < 30 AND MACD > signal line')","position sizing rules and risk parameters"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_7","uri":"capability://automation.workflow.streamlit.interactive.dashboard.and.visualization","name":"streamlit-interactive-dashboard-and-visualization","description":"Provides a web-based interactive dashboard built with Streamlit that displays strategy results, performance metrics, equity curves, and regime classifications in real-time. The dashboard enables users to explore backtest results, compare strategies, and adjust parameters without touching code, serving as the primary user interface for the entire AgentQuant platform.","intents":["I want to visualize strategy performance and backtest results in an interactive web interface","I need to compare multiple strategies side-by-side and understand their risk/return profiles","I want to adjust strategy parameters and see results update in real-time without rerunning the entire pipeline"],"best_for":["non-technical traders and portfolio managers evaluating strategies","quantitative teams presenting research results to stakeholders","traders wanting to monitor strategy performance and regime changes"],"limitations":["Streamlit is designed for data apps, not production dashboards — limited customization and styling options","Real-time updates require re-running the entire pipeline — no incremental updates or caching of intermediate results","No user authentication or multi-user support — not suitable for team collaboration or enterprise deployment","Performance degrades with large universes (1000+ symbols) due to Streamlit's single-threaded execution model"],"requires":["Python 3.9+","Streamlit 1.0+","All upstream pipeline outputs (backtest results, metrics, charts)","Web browser for dashboard access"],"input_types":["backtest results (equity curves, trade logs)","performance metrics (Sharpe ratio, drawdown, returns)","regime classifications and market context"],"output_types":["interactive web dashboard (HTML/CSS/JavaScript rendered by Streamlit)","downloadable reports (CSV, PDF with charts and metrics)","real-time metric updates and visualizations"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_8","uri":"capability://automation.workflow.configuration.driven.strategy.parameterization","name":"configuration-driven-strategy-parameterization","description":"Enables users to define strategy parameters, data ranges, risk thresholds, and regime definitions through YAML configuration files and Python config.py modules, rather than hardcoding values. This configuration-driven approach allows non-technical users to customize strategy behavior without modifying code, and enables rapid experimentation with different parameter sets.","intents":["I want to adjust strategy parameters (risk thresholds, date ranges, indicator settings) without touching code","I need to run multiple strategy variations with different configurations in batch","I want to version control strategy configurations separately from code"],"best_for":["non-technical traders and portfolio managers customizing strategies","teams managing multiple strategy configurations across environments","researchers running parameter sensitivity analysis"],"limitations":["Configuration validation is minimal — invalid parameters may cause runtime errors deep in the pipeline","No parameter bounds checking or constraint validation — users can set nonsensical values (e.g., negative position sizes)","Configuration schema is not formally defined — no schema validation or IDE autocomplete support","No built-in parameter optimization or grid search — users must manually create multiple config files for sensitivity analysis"],"requires":["Python 3.9+","YAML parser (PyYAML 6.0+)","Understanding of configuration file format and parameter meanings"],"input_types":["YAML configuration files (config.yaml)","Python configuration modules (config.py)","environment variables for API keys and secrets"],"output_types":["validated parameter dictionaries","strategy configurations ready for execution"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onepunchmonk--agentquant__cap_9","uri":"capability://image.visual.matplotlib.based.performance.visualization.and.charting","name":"matplotlib-based-performance-visualization-and-charting","description":"Generates publication-quality charts and visualizations using matplotlib, including equity curves, drawdown plots, regime classifications, indicator overlays, and performance attribution charts. These visualizations are embedded in the Streamlit dashboard and can be exported as static images or PDF reports for presentation to stakeholders.","intents":["I want to visualize strategy equity curves and drawdowns to understand performance","I need to create professional charts for presenting strategy results to investors or management","I want to see how strategies perform across different market regimes"],"best_for":["quantitative researchers and traders analyzing strategy performance","portfolio managers presenting strategy results to stakeholders","teams generating automated research reports"],"limitations":["Matplotlib is static visualization library — no interactive features like zooming, panning, or hover tooltips","Chart generation is CPU-intensive for large universes — may cause dashboard slowdowns","No built-in support for advanced financial charts (candlesticks, volume profiles) — requires custom plotting code","Chart styling is limited compared to modern visualization libraries (Plotly, Altair)"],"requires":["Python 3.9+","matplotlib 3.5+","numpy and pandas for data preparation"],"input_types":["equity curves (time series of portfolio values)","drawdown series (peak-to-trough declines)","regime classifications (Bull/Bear/Sideways labels)","indicator values and price data"],"output_types":["static PNG/PDF charts","matplotlib figure objects for embedding in Streamlit","publication-quality visualizations for reports"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Google Gemini Pro API key with quota for LLM calls","Stock symbols available in yfinance (US equities primarily)","Streamlit 1.0+ for dashboard visualization","pandas 1.3+, numpy 1.20+","OHLCV data with at least 200 historical bars per symbol","yfinance or compatible data source","All dependencies (LangChain, vectorbt, yfinance, Streamlit, Gemini Pro API key, FRED API key)","Stock symbols available in yfinance","Internet connectivity for API calls"],"failure_modes":["Gemini Pro API dependency introduces latency (1-2 minutes per strategy generation) and rate limiting constraints","Generated strategies are mathematical formulations only — no live trading execution or real-time rebalancing","Strategy quality depends entirely on feature engineering and regime detection accuracy; garbage-in-garbage-out from poor market data","No multi-asset class support — limited to equities with yfinance data availability","Fixed set of 50+ indicators — no dynamic indicator selection or custom indicator support","Indicator calculations assume sufficient historical data (typically 200+ bars); sparse data produces NaN values","No forward-fill or interpolation for missing data — requires clean OHLCV inputs","Feature matrix grows linearly with indicator count, increasing memory usage for large universes","End-to-end automation sacrifices customization — users cannot easily modify individual pipeline stages or inject custom logic","No live trading execution — strategies are research artifacts only, requiring manual implementation for live trading","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.20010091282138337,"quality":0.47,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:09.058Z","last_commit":"2026-04-26T20:31:02Z"},"community":{"stars":94,"forks":18,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=onepunchmonk--agentquant","compare_url":"https://unfragile.ai/compare?artifact=onepunchmonk--agentquant"}},"signature":"hBQRchd0KQ3e84azcpiqasPUnWn4wmTTEbBHYks0g8CRf+1Cfdd/QjSmpzloe+KnTXlyMJ8pDoDMy3H18oyhAg==","signedAt":"2026-06-22T18:31:25.705Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/onepunchmonk--agentquant","artifact":"https://unfragile.ai/onepunchmonk--agentquant","verify":"https://unfragile.ai/api/v1/verify?slug=onepunchmonk--agentquant","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"}}