--- name: trading-agents-96df6dc3161a description: >- TradingAgents is an open-source multi-agent LLM framework that analyzes a stock or crypto ticker with analyst, bull/bear researcher, trader, risk and portfolio-manager agents and returns a rated trading decision. Use when running TradingAgents from its CLI or Python API, configuring its LLM providers and data vendors, backtesting its past decisions, or building a similar multi-agent financial analysis pipeline with LangGraph. license: Apache-2.0 compatibility: "Python 3.11+ for TradingAgents; an API key for one LLM provider (or a ChatGPT Plus/Pro plan)" metadata: author: terminal-skills version: "1.1.0" category: data-ai repository: https://github.com/TauricResearch/TradingAgents tags: "trading, finance, multi-agent, llm, analysis" --- # TradingAgents — Multi-Agent LLM Financial Trading Framework ## Overview [TauricResearch/TradingAgents](https://github.com/TauricResearch/TradingAgents) (Apache-2.0, built on LangGraph, v0.7.0 of 2026-10-16 when checked) models a trading firm: analysts (market/technical, social/sentiment, news, fundamentals) work in parallel, a bull and a bear researcher debate, a trader proposes a plan, a risk team challenges it and a portfolio manager approves or rejects. Past decisions are scored against later prices and fed back as memory. It is a research tool, not investment advice. ``` Analysts: Market | Sentiment | News | Fundamentals (parallel) | Researchers: Bull <-debate-> Bear -> Trader | Risk team -> Portfolio Manager -> rated decision ``` ## Instructions ### 1. Install The package is not on PyPI. Install from a clone (Python 3.11 or newer), or from the wheel attached to a GitHub release: ```bash git clone https://github.com/TauricResearch/TradingAgents.git cd TradingAgents python3 -m venv .venv && source .venv/bin/activate pip install . ``` Docker alternative: `cp .env.example .env`, add keys, then `docker compose run --rm tradingagents`. ### 2. Configure provider and models Set the API key of one provider (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GOOGLE_API_KEY`, `XAI_API_KEY`, `DEEPSEEK_API_KEY`, `OPENROUTER_API_KEY`, `MISTRAL_API_KEY`, `GROQ_API_KEY` and others; `FRED_API_KEY` and `ALPHA_VANTAGE_API_KEY` are optional data keys). Everything else is set with `TRADINGAGENTS_*` variables, which override `DEFAULT_CONFIG`: | Variable | Config key | Meaning | |---|---|---| | `TRADINGAGENTS_LLM_PROVIDER` | `llm_provider` | `openai`, `anthropic`, `google`, `ollama`, `bedrock`, `chatgpt`, `openai_compatible`, ... | | `TRADINGAGENTS_QUICK_THINK_LLM` | `quick_think_llm` | model for analysts, researchers, trader | | `TRADINGAGENTS_DEEP_THINK_LLM` | `deep_think_llm` | model for the managers | | `TRADINGAGENTS_MAX_DEBATE_ROUNDS` | `max_debate_rounds` | bull/bear rounds (default 1) | | `TRADINGAGENTS_MAX_RISK_ROUNDS` | `max_risk_discuss_rounds` | risk-team rounds (default 1) | | `TRADINGAGENTS_OUTPUT_LANGUAGE` | `output_language` | language of reports | | `TRADINGAGENTS_RESULTS_DIR` | `results_dir` | where reports go (default `~/.tradingagents/logs`) | Each tier can use its own provider (`TRADINGAGENTS_QUICK_THINK_PROVIDER`, `TRADINGAGENTS_DEEP_THINK_PROVIDER`). Model names come from the provider's current catalog; the shipped defaults are `gpt-6-luna` (quick) and `gpt-6-sol` (deep). Data comes from yfinance, SEC EDGAR (fundamentals), FRED and Polymarket by default; change vendors in the `data_vendors` config dict. ### 3. Run from the CLI `tradingagents` opens an interactive screen (ticker, date, analysts, provider, depth). For scripts and cron, pass flags and the rest through variables: ```bash export TRADINGAGENTS_LLM_PROVIDER=openai TRADINGAGENTS_QUICK_THINK_LLM=gpt-6-luna TRADINGAGENTS_DEEP_THINK_LLM=gpt-6-sol tradingagents --ticker NVDA --date 2026-09-23 --analysts market,news,fundamentals --save --no-show ``` Other flags: `--checkpoint` (resume a crashed run), `--portfolio holdings.json` (size against your real position), `--html/--no-html`. Subcommands: `tradingagents backtest NVDA,AAPL --start 2026-06-01 --end 2026-09-01 --every 7` scores past decisions over a ticker/date grid (`--start` and `--end` are required); `tradingagents login chatgpt` signs in a ChatGPT Plus/Pro plan for the `chatgpt` provider. Tickers use Yahoo Finance symbols: `AAPL`, `0700.HK`, `7203.T`, `BTC-USD`. ### 4. Use from Python ```python from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG config = DEFAULT_CONFIG.copy() config["max_debate_rounds"] = 2 ta = TradingAgentsGraph(selected_analysts=("market", "news", "fundamentals"), debug=True, config=config) state, decision = ta.propagate("NVDA", "2026-09-01") # (ticker, analysis date); pass asset_type="crypto" for BTC-USD print(decision) # Buy, Overweight, Hold, Underweight, Sell, or REVIEW when no rating could be parsed ta.save_reports(state, "NVDA") # report tree under results_dir/reports ``` ### 5. Build a lighter pipeline yourself For a custom, smaller version of the same idea, this LangGraph graph (tested with a stub LLM: 7 model calls, analysts in parallel, one risk and one portfolio step) uses real price data and passes facts, not guesses, to the agents. Install `langgraph langchain-anthropic yfinance`. ```python import os from typing import TypedDict import yfinance as yf from langchain_anthropic import ChatAnthropic from langgraph.graph import StateGraph, START, END class TradingState(TypedDict, total=False): ticker: str fundamentals: str technicals: str news: str bull: str bear: str risk: str decision: str def build_graph(llm): def ask(prompt: str) -> str: return llm.invoke(prompt).content def fundamentals(s: TradingState): info = yf.Ticker(s["ticker"]).info keys = ["trailingPE", "forwardPE", "revenueGrowth", "profitMargins", "debtToEquity"] facts = {k: info.get(k) for k in keys} return {"fundamentals": ask(f"Assess valuation, profitability and balance sheet of {s['ticker']} from {facts}.")} def technicals(s: TradingState): close = yf.Ticker(s["ticker"]).history(period="1y")["Close"] facts = {"last": round(close.iloc[-1], 2), "ma50": round(close.tail(50).mean(), 2), "ma200": round(close.tail(200).mean(), 2)} return {"technicals": ask(f"Rate the trend of {s['ticker']} as bullish, neutral or bearish from {facts}.")} def news(s: TradingState): headlines = [n.get("content", {}).get("title", "") for n in yf.Ticker(s["ticker"]).news[:10]] return {"news": ask(f"Rate sentiment for {s['ticker']} from these headlines: {headlines}")} def bull(s: TradingState): return {"bull": ask(f"Argue the bull case for {s['ticker']} using only: {s['fundamentals']} | {s['technicals']} | {s['news']}")} def bear(s: TradingState): return {"bear": ask(f"Argue the bear case for {s['ticker']} using only: {s['fundamentals']} | {s['technicals']} | {s['news']}")} def risk(s: TradingState): return {"risk": ask(f"Bull: {s['bull']}\nBear: {s['bear']}\nGive downside, position size as % of portfolio and a stop-loss rule.")} def portfolio(s: TradingState): return {"decision": ask(f"Write the decision for {s['ticker']} (BUY, HOLD or SELL, conviction, horizon, key risks) given Bull: {s['bull']} Bear: {s['bear']} Risk: {s['risk']}")} g = StateGraph(TradingState) for name, fn in [("fundamentals", fundamentals), ("technicals", technicals), ("news", news), ("bull", bull), ("bear", bear), ("risk", risk), ("portfolio", portfolio)]: g.add_node(name, fn) for a in ("fundamentals", "technicals", "news"): g.add_edge(START, a) # analysts run in parallel g.add_edge(["fundamentals", "technicals", "news"], "bull") # wait for all three g.add_edge(["fundamentals", "technicals", "news"], "bear") g.add_edge(["bull", "bear"], "risk") g.add_edge("risk", "portfolio") g.add_edge("portfolio", END) return g.compile() if __name__ == "__main__": llm = ChatAnthropic(model=os.environ["ANTHROPIC_MODEL"]) print(build_graph(llm).invoke({"ticker": "NVDA"})["decision"]) ``` ## Examples ### Example 1: Scheduled morning screen Request: "Run TradingAgents on NVDA, AMD and TSM every weekday and save the reports." ```bash for t in NVDA AMD TSM; do tradingagents --ticker "$t" --date "$(date +%F)" --analysts market,news,fundamentals --save --no-show done ``` Result: one report folder per ticker under `~/.tradingagents/logs` (including `complete_report.html`) with each analyst's report, the debate and the final rated decision. Run it after the US close; a run dated *d* only reads news published after the close on *d*. ### Example 2: Cheaper decisions on a local model Request: "I want to try this without paying per token." ```bash ollama pull qwen3:14b export TRADINGAGENTS_LLM_PROVIDER=ollama TRADINGAGENTS_QUICK_THINK_LLM=qwen3:14b TRADINGAGENTS_DEEP_THINK_LLM=qwen3:14b tradingagents --ticker AAPL --date 2026-09-23 --analysts market,fundamentals --no-show ``` Result: the same pipeline against `http://localhost:11434/v1` (set `OLLAMA_BASE_URL` for a remote server). Expect weaker reasoning and more format slips than with a frontier model. ## Guidelines - A full run makes many model and tool calls (each analyst loops over tools, then the debates run). Start with `--analysts market,news` and one debate round to limit cost. - Use a fast model for the quick tier and a stronger one for the deep tier; set per-tier providers if they differ. - Never run it with a date in the future or reuse a decision as if it were produced before its date; the framework filters data to the analysis date, your own code must do the same. - Backtest (`tradingagents backtest --start ... --end ...`) before believing any rating; LLM output varies between runs even at low temperature. - The framework simulates orders only. Do not wire its output to a broker without your own limits, review and legal check. - Not financial advice. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/trading-agents/SKILL.md Attribution basis: Source repository record Fields of work: Finance & Accounting, Data & Analytics, Development & Engineering, Product Management, Marketing Roles: Financial Analyst, Data Scientist, ML / AI Engineer, Product Manager, Growth Marketer Specialties: API, Backend Tools: Langchain_Anthropic, Yfinance Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 31c680ae01cec961bff5eefd8e310bef1e35a7f37019f5ad6acb24a3653e2e14