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Documentation Index

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QuantAgent decomposes the trading analysis problem into four focused agents, each responsible for a distinct analytical layer. A LangGraph StateGraph wires them into a sequential pipeline that runs from market data ingestion through to a final LONG/SHORT order.

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Graph structure

The compiled graph follows a strict linear topology:
1

START → Indicator Agent

Raw OHLCV data enters the graph. The Indicator Agent computes momentum and oscillator values using five TA-Lib tools.
2

Indicator Agent → Pattern Agent

Indicator results and an indicator_report are written into the shared state. The Pattern Agent reads this state and generates a candlestick chart for visual pattern recognition.
3

Pattern Agent → Trend Agent

A pattern_report is appended to state. The Trend Agent generates a trendline-annotated chart and uses a vision LLM to interpret support and resistance dynamics.
4

Trend Agent → Decision Maker

A trend_report is appended to state. The Decision Maker reads all three reports simultaneously and issues a structured JSON trade decision.
5

Decision Maker → END

The final_trade_decision JSON is written into state and the graph terminates.
In code, the graph is assembled by SetGraph.set_graph() in graph_setup.py: 
graph = StateGraph(IndicatorAgentState)

graph.add_edge(START, "Indicator Agent")
graph.add_edge("Indicator Agent", "Pattern Agent")
graph.add_edge("Pattern Agent", "Trend Agent")
graph.add_edge("Trend Agent", "Decision Maker")
graph.add_edge("Decision Maker", END)

return graph.compile()

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Shared state

All four agents communicate exclusively through a single IndicatorAgentState TypedDict defined in agent_state.py. No agent calls another directly — every result is written to state and read by the next node.

Input fields

kline_data, time_frame, stock_name — provided by the caller before graph invocation.

Indicator Agent writes

rsi, macd, macd_signal, macd_hist, stoch_k, stoch_d, roc, willr, indicator_report

Pattern Agent writes

pattern_image, pattern_image_filename, pattern_image_description, pattern_report

Trend Agent writes

trend_image, trend_image_filename, trend_image_description, trend_report
The Decision Maker reads indicator_report, pattern_report, and trend_report and writes final_trade_decision.

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Two LLM roles

QuantAgent uses two distinct LLM instances with different capability requirements:
RoleConfig keyDefault modelResponsibilities
graph_llmgraph_llm_modelgpt-4oPrimary LLM — used by the Indicator agent for tool-calling and report generation, and by the Pattern and Trend agents for vision-based chart analysis and the Decision agent for final synthesis. Must be vision-capable.
agent_llmagent_llm_modelgpt-4o-miniTool-dispatch LLM — used only in the Pattern and Trend agents to call the image-generation tools (generate_kline_image, generate_trend_image). A lighter model is sufficient here.
The Indicator Agent uses graph_llm exclusively. The Pattern and Trend agents use agent_llm for the tool-dispatch step (generating charts) and graph_llm for the vision analysis step (interpreting charts). The Decision Agent uses graph_llm for final synthesis.

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Why vision-capable LLMs are required

The Pattern Agent and Trend Agent both encode candlestick charts as base64 PNG images and pass them directly to the LLM using the image_url content type: 
image_prompt = [
    {"type": "text", "text": "This is a candlestick chart..."},
    {
        "type": "image_url",
        "image_url": {"url": f"data:image/png;base64,{image_b64}"},
    },
]
Any model you assign to graph_llm must support multimodal (vision) inputs. OpenAI gpt-4o, Anthropic Claude 3+ models, and Qwen VL models all satisfy this requirement.
Assigning a text-only model to graph_llm will cause runtime errors in the Pattern Agent and Trend Agent nodes. Always verify that your chosen model supports image inputs before running the graph.

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Supported LLM providers

TradingGraph supports three providers, configured via the agent_llm_provider and graph_llm_provider config keys:
  • openai — ChatOpenAI (e.g., gpt-4o, gpt-4o-mini)
  • anthropic — ChatAnthropic (e.g., claude-3-5-sonnet-20241022)
  • qwen — ChatQwen (e.g., qwen-vl-max-latest)

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