{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-tauricresearch--tradingagents","slug":"tauricresearch--tradingagents","name":"TradingAgents","type":"agent","url":"https://arxiv.org/pdf/2412.20138","page_url":"https://unfragile.ai/tauricresearch--tradingagents","categories":["chatbots-assistants"],"tags":["agent","finance","llm","multiagent","trading"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"pending_review","verified":false},"capabilities":[{"id":"github-tauricresearch--tradingagents__cap_0","uri":"capability://planning.reasoning.multi.agent.sequential.trading.decision.pipeline","name":"multi-agent sequential trading decision pipeline","description":"Orchestrates a five-phase sequential workflow (Analyst Team → Research Team → Trader Agent → Risk Management Team → Portfolio Manager) using LangGraph state machines, where each phase processes market data and prior outputs to generate progressively refined trading decisions. Implements state propagation across agent boundaries with explicit message passing and reflection loops, enabling structured reasoning chains where later agents build on earlier analysis.","intents":["I want to coordinate multiple specialized LLM agents to analyze a stock and generate a trading decision with clear reasoning at each stage","I need to ensure trading decisions pass through multiple validation layers (analysis, research, risk assessment) before execution","I want to see how different agent perspectives (bullish, bearish, risk-aware) contribute to a final portfolio decision"],"best_for":["quantitative trading teams building LLM-driven decision systems","fintech startups prototyping multi-agent trading frameworks","researchers studying emergent behavior in agent coordination"],"limitations":["Sequential pipeline introduces latency — each phase must complete before next begins, no parallel cross-phase optimization","State explosion risk with complex market conditions — context window constraints limit historical data passed between phases","No built-in rollback or transaction semantics — if risk management rejects a trade, prior analysis is discarded without learning","Determinism issues with LLM outputs — same market data may produce different decisions across runs, complicating backtesting"],"requires":["Python 3.9+","LangGraph 0.1.0+","API key for at least one LLM provider (OpenAI, Anthropic, Google, xAI, OpenRouter, or local Ollama)","Real-time or historical market data source (Yahoo Finance, Alpha Vantage, or local cache)"],"input_types":["ticker symbol (string)","market data (OHLCV candles, technical indicators)","configuration parameters (LLM model names, risk thresholds, analysis depth)"],"output_types":["structured trading decision (buy/sell/hold with confidence score)","reasoning trace (analyst reports, debate transcripts, risk assessments)","portfolio action (position size, stop-loss, take-profit levels)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.client.factory.with.unified.interface","name":"multi-provider llm client factory with unified interface","description":"Provides a unified client factory that abstracts six LLM providers (OpenAI, Anthropic, Google, xAI, OpenRouter, Ollama) behind a single interface, enabling runtime provider switching without code changes. Implements provider detection via configuration, model instantiation with provider-specific parameters, and fallback logic for API failures, allowing agents to use different models for different reasoning tasks (deep vs quick thinking).","intents":["I want to switch between OpenAI, Anthropic, and local Ollama models without rewriting agent code","I need to use different LLM providers for different reasoning depths (expensive Claude for analysis, fast GPT-4o mini for synthesis)","I want to avoid vendor lock-in and test trading decisions across multiple LLM backends"],"best_for":["teams evaluating multiple LLM providers for trading applications","cost-conscious builders wanting to mix expensive and cheap models strategically","researchers comparing LLM reasoning quality across providers"],"limitations":["No automatic prompt normalization — provider-specific prompt formats (system/user roles, function calling schemas) require manual adaptation","Token counting varies by provider — cost estimation and context window management must account for provider-specific tokenization","Latency differences not abstracted — OpenAI may respond in 500ms while Ollama takes 5s, requiring caller-side timeout handling","API rate limits and quota management are provider-specific — no unified backoff or retry strategy"],"requires":["Python 3.9+","API keys for selected providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, XAI_API_KEY, OPENROUTER_API_KEY)","Ollama running locally on port 11434 (for local inference option)","Configuration file specifying provider and model names"],"input_types":["provider name (string: 'openai', 'anthropic', 'google', 'xai', 'openrouter', 'ollama')","model identifier (string: 'gpt-4', 'claude-3-opus', 'gemini-2.0-flash', etc.)","LLM parameters (temperature, max_tokens, top_p)"],"output_types":["LLM client instance (provider-specific ChatModel object)","completion response (text or structured output)","token usage metadata (input_tokens, output_tokens)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_10","uri":"capability://planning.reasoning.trader.agent.with.position.sizing.and.execution.parameters","name":"trader agent with position sizing and execution parameters","description":"Implements a trader agent that synthesizes analyst reports and debate outcomes into a unified trading decision with specific execution parameters: action (buy/sell/hold), confidence score (0-1), position size (percentage of portfolio), entry price, stop-loss, and take-profit levels. Uses deep thinking LLM to reason about position sizing based on confidence, volatility, and portfolio constraints. Outputs are structured for downstream execution systems.","intents":["I want to convert analyst reports and debate outcomes into a specific trading action with position sizing","I need entry, stop-loss, and take-profit levels for execution, not just buy/sell signals","I want position sizing to be proportional to confidence and account for portfolio constraints"],"best_for":["trading systems that need specific execution parameters (entry, stop-loss, take-profit) from analysis","portfolio managers wanting to size positions based on confidence and risk","execution systems that need structured trade specifications for order placement"],"limitations":["Position sizing is heuristic-based — no optimization against historical returns or Sharpe ratio","Entry/stop-loss/take-profit levels are LLM-generated — may not align with technical support/resistance or volatility-based levels","No account for portfolio constraints in trader agent — relies on downstream risk management to reject oversized positions","Confidence scores are subjective — no calibration against actual prediction accuracy"],"requires":["Python 3.9+","Analyst reports (from analyst team phase)","Debate outcomes (from research team phase)","Market data (current price, volatility)","LLM provider configured for deep thinking"],"input_types":["analyst reports (text summaries)","bullish and bearish research reports (text)","current market data (price, volatility)"],"output_types":["trading decision (dict with action, confidence, position_size, entry_price, stop_loss, take_profit)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_11","uri":"capability://tool.use.integration.extensible.agent.architecture.with.custom.agent.creation","name":"extensible agent architecture with custom agent creation","description":"Provides a framework for creating custom agents by extending base agent classes and implementing agent-specific logic (data gathering, reasoning, output formatting). Agents are registered in the LangGraph graph and receive state as input, producing outputs that are added to shared state. Supports agent tools (data fetching, calculations) that agents can invoke during reasoning. Enables teams to add domain-specific agents (e.g., ESG analyst, options analyst) without modifying core framework.","intents":["I want to add a custom analyst agent (e.g., ESG analyst, options analyst) without modifying core framework code","I need to create agents that use domain-specific tools and data sources","I want to extend the pipeline with new phases or agent types"],"best_for":["teams building specialized trading systems with custom analysis requirements","researchers experimenting with new agent types and reasoning patterns","developers extending TradingAgents for domain-specific use cases"],"limitations":["Agent interface is loosely defined — no strict contract for input/output types, leading to integration issues","Tool invocation is manual — agents must explicitly call tools, no automatic tool discovery or binding","State management is implicit — custom agents must understand shared state structure to read/write correctly","Testing custom agents requires full pipeline setup — no isolated unit testing framework"],"requires":["Python 3.9+","Understanding of LangGraph state machines and agent patterns","Base agent class (from tradingagents/agents/)","LLM provider configured"],"input_types":["agent class definition (extending base agent)","agent tools (functions for data fetching, calculations)","state input (from prior pipeline phases)"],"output_types":["agent output (structured report or decision)","updated state (with agent outputs added)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_12","uri":"capability://text.generation.language.deep.vs.quick.thinking.model.strategy.with.latency.optimization","name":"deep vs quick thinking model strategy with latency optimization","description":"Implements a dual-model strategy where complex reasoning tasks (analyst reports, research debate, risk assessment) use deep_think_llm (expensive, high-quality models like Claude 3 Opus), while rapid synthesis tasks use quick_think_llm (fast, cost-effective models like GPT-4o mini). Configuration allows per-task model assignment without code changes. Reduces overall latency and cost compared to using expensive models for all tasks, while maintaining reasoning quality where it matters most.","intents":["I want to use expensive, high-quality models for complex analysis but fast, cheap models for synthesis to optimize cost and latency","I need to balance reasoning quality with execution speed — deep thinking for critical decisions, quick thinking for routine tasks","I want to experiment with different model combinations (e.g., Claude for analysis, GPT-4o mini for synthesis) without code changes"],"best_for":["cost-conscious trading teams that want to optimize model selection per task","systems running high-frequency analyses where latency matters","teams experimenting with model combinations to find optimal cost/quality tradeoff"],"limitations":["Model quality mismatch — quick_think_llm may produce lower-quality synthesis if reasoning is complex","No automatic task classification — must manually assign tasks to deep vs quick thinking, no heuristic-based selection","Latency savings depend on model choice — if quick_think_llm is slow (e.g., local Ollama), savings are minimal","Cost savings are model-dependent — if quick_think_llm is expensive (e.g., Claude), cost advantage is lost"],"requires":["Python 3.9+","Two LLM providers configured (deep_think_llm and quick_think_llm)","API keys for both providers"],"input_types":["deep_think_model (string: model name for complex reasoning)","quick_think_model (string: model name for rapid synthesis)"],"output_types":["task assignments (which tasks use deep vs quick thinking)","latency and cost metrics (per-task execution time and token usage)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_2","uri":"capability://data.processing.analysis.data.vendor.abstraction.with.fallback.chain.and.caching","name":"data vendor abstraction with fallback chain and caching","description":"Implements a vendor router (route_to_vendor) that abstracts market data acquisition across multiple sources (Yahoo Finance, Alpha Vantage, local cache) with automatic fallback logic. When primary vendor fails or rate-limits, the system transparently retries with secondary vendors, and caches results locally to reduce API calls and improve latency. Technical indicators (RSI, MACD, Bollinger Bands) are computed on-demand and cached per ticker.","intents":["I want to fetch market data from Yahoo Finance, but fall back to Alpha Vantage if Yahoo is down or rate-limited","I need to cache historical price data locally to avoid repeated API calls during backtesting or multiple agent analyses","I want to compute technical indicators once and reuse them across multiple agents without recalculation"],"best_for":["trading systems requiring high availability and resilience to API outages","backtesting frameworks that run thousands of analyses and need to minimize API costs","teams running agents on limited API budgets (free tiers of Alpha Vantage, Yahoo Finance)"],"limitations":["Cache invalidation is time-based only — no real-time cache refresh, stale data may be used if market gaps exceed cache TTL","Fallback chain is sequential and blocking — if primary vendor is slow, system waits for timeout before trying secondary","Data consistency issues across vendors — Yahoo Finance and Alpha Vantage may report slightly different OHLCV values for same date","Technical indicator caching assumes market data is immutable — if cache is updated mid-day, indicators become inconsistent"],"requires":["Python 3.9+","yfinance library (for Yahoo Finance integration)","Alpha Vantage API key (optional, for fallback)","Local filesystem access for cache storage (default: ./data/cache/)","Network connectivity for API calls"],"input_types":["ticker symbol (string: 'AAPL', 'MSFT')","date range (start_date, end_date as datetime objects)","indicator parameters (period for RSI, MACD, Bollinger Bands)"],"output_types":["OHLCV data (DataFrame with Open, High, Low, Close, Volume columns)","technical indicators (DataFrame with RSI, MACD, Bollinger Bands columns)","metadata (data source, fetch timestamp, cache hit/miss status)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_3","uri":"capability://planning.reasoning.structured.bull.bear.debate.system.with.synthesis","name":"structured bull/bear debate system with synthesis","description":"Implements a two-researcher debate phase where one researcher generates bullish arguments and another generates bearish arguments for a given ticker, using structured prompts that enforce opposing viewpoints. A trader agent then synthesizes both perspectives into a unified trading decision (buy/sell/hold with confidence score and position sizing), ensuring the final decision accounts for both upside and downside risks rather than relying on single-perspective analysis.","intents":["I want to generate both bullish and bearish perspectives on a stock before making a trading decision","I need to ensure my trading system considers counterarguments and downside risks, not just bullish signals","I want to see the reasoning behind both sides of a trade decision for explainability and audit trails"],"best_for":["risk-conscious trading teams that want to avoid confirmation bias in LLM-driven decisions","compliance-heavy organizations requiring documented reasoning for trading decisions","researchers studying how multi-perspective reasoning improves LLM decision quality"],"limitations":["Debate is sequential, not adversarial — researchers don't see each other's arguments, so no true back-and-forth debate","Synthesis bias toward bullish arguments — if LLM has inherent optimism bias, synthesis may weight bullish views higher despite equal prompt emphasis","No weighting mechanism for debate outcomes — both perspectives contribute equally regardless of argument strength or data quality","Debate adds latency — two additional LLM calls (bull + bear researchers) before trader synthesis, increasing total pipeline time"],"requires":["Python 3.9+","LLM provider configured for deep thinking (used for both researchers and trader)","Market data for ticker (from data vendor layer)","Analyst reports from prior pipeline phase (used as context for debate)"],"input_types":["ticker symbol (string)","analyst reports (text summaries from prior phase)","market data (OHLCV, technical indicators)","debate prompts (system prompts enforcing bull/bear perspectives)"],"output_types":["bullish research report (text with bull case arguments)","bearish research report (text with bear case arguments)","synthesized trading decision (action: buy/sell/hold, confidence: 0-1, position_size: percentage)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_4","uri":"capability://planning.reasoning.risk.management.multi.agent.assessment.with.portfolio.approval","name":"risk management multi-agent assessment with portfolio approval","description":"Implements a three-agent risk management team (Value-at-Risk agent, Correlation agent, Liquidity agent) that independently evaluates proposed trades against portfolio-level constraints, followed by a Portfolio Manager agent that approves or rejects trades based on aggregated risk assessments. Each risk agent uses deep thinking to analyze different risk dimensions, and the Portfolio Manager synthesizes their outputs with portfolio state to make final approval decisions.","intents":["I want to validate proposed trades against portfolio-level risk metrics (VaR, correlation, liquidity) before execution","I need multiple independent risk perspectives to assess whether a trade fits my portfolio constraints","I want a final approval gate that can reject trades that pass individual analysis but violate portfolio rules"],"best_for":["institutional trading desks with strict risk limits and compliance requirements","portfolio managers wanting to automate pre-trade risk checks","teams building risk-aware trading systems that need explainable rejection reasons"],"limitations":["Risk assessment is retrospective — uses historical data and current portfolio state, cannot predict future correlation changes","No dynamic risk limit adjustment — VaR, correlation, liquidity thresholds are static configuration, not adaptive to market regime changes","Portfolio Manager has final veto but no override mechanism — rejected trades cannot be force-approved, limiting manual intervention","Risk agents operate independently without feedback loops — if one agent's assessment is wrong, others don't learn or adjust"],"requires":["Python 3.9+","Current portfolio state (holdings, positions, cash balance)","Historical price data for portfolio constituents (for correlation calculation)","Risk parameters (VaR confidence level, correlation threshold, liquidity minimum)","LLM provider configured for deep thinking"],"input_types":["proposed trade (ticker, action: buy/sell, quantity, price)","current portfolio (holdings DataFrame with quantities and prices)","market data (historical prices for correlation, volume for liquidity)","risk configuration (VaR_confidence, correlation_threshold, liquidity_threshold)"],"output_types":["VaR assessment (estimated portfolio loss at confidence level, pass/fail)","correlation assessment (correlation with portfolio, diversification impact, pass/fail)","liquidity assessment (estimated slippage, execution feasibility, pass/fail)","portfolio approval decision (approved/rejected with reason)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_5","uri":"capability://automation.workflow.interactive.cli.with.real.time.display.and.configuration.flow","name":"interactive cli with real-time display and configuration flow","description":"Provides an interactive command-line interface (cli/main.py) using questionary prompts for user configuration, Rich library for real-time formatted output, and a message buffer system that displays agent reasoning, debate transcripts, and risk assessments as they execute. Supports interactive configuration of LLM providers, model names, risk parameters, and data sources, with validation and defaults for each parameter.","intents":["I want to configure a trading analysis interactively without editing config files","I need to see agent reasoning and debate transcripts in real-time as the pipeline executes","I want formatted, color-coded output that shows which phase is running and what decisions are being made"],"best_for":["traders and analysts who want to run ad-hoc analyses without coding","teams demoing the system to stakeholders and needing visual feedback","developers debugging agent behavior and needing to see intermediate outputs"],"limitations":["Interactive prompts block execution — cannot run multiple analyses in parallel from CLI","Real-time display is terminal-only — no web UI or remote monitoring capability","Message buffer has fixed size — very long agent outputs may be truncated or lost","Configuration is session-scoped — changes are not persisted to disk, must reconfigure on next run"],"requires":["Python 3.9+","questionary library (for interactive prompts)","Rich library (for formatted terminal output)","Terminal with ANSI color support (for Rich formatting)","All LLM provider dependencies (OpenAI, Anthropic, etc.)"],"input_types":["user responses to questionary prompts (ticker, LLM provider, model name, risk parameters)","configuration defaults (from DEFAULT_CONFIG or config file)"],"output_types":["formatted terminal output (colored text, tables, progress indicators)","agent reasoning traces (analyst reports, debate transcripts, risk assessments)","final trading decision (formatted summary with confidence and position sizing)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_6","uri":"capability://tool.use.integration.programmatic.python.api.with.state.propagation","name":"programmatic python api with state propagation","description":"Exposes a programmatic Python API (TradingAgentsGraph.propagate()) that allows developers to invoke the trading pipeline from code, passing configuration and ticker symbols, and receiving structured outputs (trading decisions, reasoning traces, risk assessments). The API manages state propagation across agent phases using LangGraph state machines, handling message passing, reflection updates, and phase transitions automatically without requiring manual orchestration.","intents":["I want to call the trading pipeline from my own Python code without using the CLI","I need to integrate trading decisions into a larger system (portfolio management, execution, backtesting)","I want to programmatically iterate over multiple tickers and collect results for analysis"],"best_for":["developers building trading systems that embed TradingAgents as a component","quants running backtests that need to invoke the pipeline for each historical date","teams integrating trading decisions into execution systems or portfolio managers"],"limitations":["Synchronous API only — propagate() blocks until pipeline completes, no async support for parallel analyses","State is ephemeral — no persistence between calls, each propagate() invocation starts fresh without learning from prior decisions","Error handling is basic — exceptions in agent phases propagate to caller without recovery or fallback logic","No streaming output — caller must wait for entire pipeline to complete before receiving results"],"requires":["Python 3.9+","TradingAgents package installed","LLM provider API keys configured","Market data source configured (Yahoo Finance, Alpha Vantage, or cache)"],"input_types":["configuration dict (llm_provider, model_names, risk_parameters, data_source)","ticker symbol (string)","optional: date range for historical analysis"],"output_types":["trading decision (dict with action, confidence, position_size)","reasoning traces (dict with analyst_reports, debate_transcripts, risk_assessments)","execution metadata (phase durations, token usage, data sources used)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_7","uri":"capability://tool.use.integration.configuration.system.with.llm.provider.and.model.selection","name":"configuration system with llm provider and model selection","description":"Implements a configuration system (DEFAULT_CONFIG, config files, environment variables) that centralizes LLM provider selection, model names, risk parameters, and data source configuration. Supports runtime configuration via CLI prompts or programmatic API, with validation and defaults for each parameter. Enables per-agent model assignment (deep_think_llm for complex reasoning, quick_think_llm for rapid tasks) without code changes.","intents":["I want to switch LLM providers and models without modifying code","I need to configure different models for different reasoning tasks (expensive Claude for analysis, cheap GPT for synthesis)","I want to set risk parameters (VaR threshold, correlation limit) without touching agent code"],"best_for":["teams experimenting with different LLM providers and model combinations","cost-conscious builders wanting to optimize model selection per task","operators managing trading systems across different environments (dev, staging, prod)"],"limitations":["Configuration is flat — no hierarchical or nested config for complex agent-specific settings","No runtime config validation — invalid model names or provider keys are only caught when agents execute","Environment variable support is basic — no templating or variable substitution","Configuration changes require pipeline restart — no hot-reload or dynamic reconfiguration"],"requires":["Python 3.9+","Configuration file (YAML or JSON) or environment variables","LLM provider API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)"],"input_types":["configuration dict (llm_provider, deep_think_model, quick_think_model, risk_parameters)","environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","config file (YAML or JSON with provider and model settings)"],"output_types":["validated configuration object (Config class with all parameters)","LLM client instances (instantiated for deep and quick thinking models)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_8","uri":"capability://memory.knowledge.state.management.and.reflection.with.memory.updates","name":"state management and reflection with memory updates","description":"Implements LangGraph-based state management that tracks agent outputs, reasoning traces, and portfolio state across pipeline phases. Includes reflection loops where agents review prior outputs and update memory (e.g., analyst agents reflect on their own reports, risk agents update memory with assessment results). State is propagated between phases via explicit message passing, with each phase reading prior outputs and adding new information to shared state.","intents":["I want agents to see prior analysis and build on it, not start from scratch each phase","I need to track reasoning traces and decisions throughout the pipeline for audit and debugging","I want agents to reflect on their own outputs and update memory based on new information"],"best_for":["teams building auditable trading systems that need full decision traces","researchers studying how agent memory and reflection improve decision quality","systems requiring compliance documentation of reasoning for each decision"],"limitations":["State explosion with long pipelines — context window constraints limit how much prior state can be passed to later agents","Reflection loops are one-way — agents update memory but don't receive feedback on whether their updates were useful","No state compression — entire reasoning traces are stored, leading to large state objects and high memory usage","State is pipeline-scoped — no persistence between runs, memory is lost after pipeline completes"],"requires":["Python 3.9+","LangGraph 0.1.0+","Sufficient context window in LLM to handle accumulated state (typically 8k+ tokens)"],"input_types":["agent outputs (text reports, structured decisions)","prior state (from previous pipeline phases)","reflection prompts (instructions for agents to review and update memory)"],"output_types":["updated state (with new agent outputs and memory updates)","reasoning traces (full history of agent reasoning across phases)","memory updates (structured records of what agents learned)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tauricresearch--tradingagents__cap_9","uri":"capability://planning.reasoning.analyst.team.with.parallel.multi.perspective.analysis","name":"analyst team with parallel multi-perspective analysis","description":"Implements four parallel analyst agents (Fundamental, Technical, Sentiment, Macro) that independently analyze a ticker from different perspectives using deep thinking LLM. Each analyst generates a structured report with key findings, signals, and confidence scores. Reports are collected and passed to the research team phase, enabling multi-perspective analysis without sequential bottlenecks. Analysts use market data (OHLCV, technical indicators) and can invoke data tools to fetch additional information.","intents":["I want to analyze a stock from multiple perspectives (fundamental, technical, sentiment, macro) in parallel","I need independent analyst reports that I can compare and synthesize into a trading decision","I want each analyst to use domain-specific tools and data (fundamental: financial ratios, technical: indicators, sentiment: news, macro: economic data)"],"best_for":["trading systems that want comprehensive multi-perspective analysis without sequential latency","teams building explainable trading decisions that require documented reasoning from multiple angles","researchers studying how different analysis perspectives contribute to trading performance"],"limitations":["Analysts operate independently — no cross-analyst feedback or debate at analysis stage, only at research stage","Analyst prompts are generic — no domain-specific knowledge injection (e.g., fundamental analyst doesn't have access to financial statement data)","Parallel execution adds complexity — requires async/concurrent execution, error handling across multiple agents","Report aggregation is manual — no automatic conflict resolution if analysts disagree on signals"],"requires":["Python 3.9+","LLM provider configured for deep thinking","Market data (OHLCV, technical indicators)","Optional: fundamental data (financial ratios), sentiment data (news), macro data (economic indicators)"],"input_types":["ticker symbol (string)","market data (OHLCV DataFrame, technical indicators)","optional: fundamental metrics, sentiment scores, macro indicators"],"output_types":["fundamental analyst report (text with key findings, signals, confidence)","technical analyst report (text with key findings, signals, confidence)","sentiment analyst report (text with key findings, signals, confidence)","macro analyst report (text with key findings, signals, confidence)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LangGraph 0.1.0+","API key for at least one LLM provider (OpenAI, Anthropic, Google, xAI, OpenRouter, or local Ollama)","Real-time or historical market data source (Yahoo Finance, Alpha Vantage, or local cache)","API keys for selected providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, XAI_API_KEY, OPENROUTER_API_KEY)","Ollama running locally on port 11434 (for local inference option)","Configuration file specifying provider and model names","Analyst reports (from analyst team phase)","Debate outcomes (from research team phase)","Market data (current price, volatility)"],"failure_modes":["Sequential pipeline introduces latency — each phase must complete before next begins, no parallel cross-phase optimization","State explosion risk with complex market conditions — context window constraints limit historical data passed between phases","No built-in rollback or transaction semantics — if risk management rejects a trade, prior analysis is discarded without learning","Determinism issues with LLM outputs — same market data may produce different decisions across runs, complicating backtesting","No automatic prompt normalization — provider-specific prompt formats (system/user roles, function calling schemas) require manual adaptation","Token counting varies by provider — cost estimation and context window management must account for provider-specific tokenization","Latency differences not abstracted — OpenAI may respond in 500ms while Ollama takes 5s, requiring caller-side timeout handling","API rate limits and quota management are provider-specific — no unified backoff or retry strategy","Position sizing is heuristic-based — no optimization against historical returns or Sharpe ratio","Entry/stop-loss/take-profit levels are LLM-generated — may not align with technical support/resistance or volatility-based levels","builder identity is not verified yet","artifact is still pending 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