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Documentation Index

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QuantAgent uses four specialized agent nodes connected in a fixed sequence: 
Indicator Agent → Pattern Agent → Trend Agent → Decision Maker
Each agent is created by a factory function that returns a LangGraph-compatible node function (a callable that accepts a state dict and returns a partial state dict). 
from indicator_agent import create_indicator_agent
Creates the Indicator Agent node. This agent computes five technical indicators over the OHLCV data and produces a narrative analysis report.Parameters
ParameterTypeDescription
llmBaseChatModelThe LLM that calls indicator tools and synthesizes the report. Bound with tool-calling capability.
toolkitTechnicalToolsToolkit instance exposing the indicator methods as LangChain tools.
Tools used
  • compute_macd — MACD line, signal, and histogram
  • compute_rsi — Relative Strength Index
  • compute_roc — Rate of Change
  • compute_stoch — Stochastic Oscillator %K and %D
  • compute_willr — Williams %R
Execution modelThe agent uses a tool-call loop with a maximum of 5 iterations. On each iteration it checks whether the LLM has emitted further tool calls. Once the LLM produces a plain-text response with no additional tool calls, the loop exits and the text is stored as indicator_report.If the LLM returns empty content (common with multi-step Claude responses), the agent scans backwards through the message history for the most recent non-empty text content.State output
KeyTypeDescription
indicator_reportstrNarrative summary of all computed indicators.
messageslistUpdated message list including all tool calls and responses.
from pattern_agent import create_pattern_agent
Creates the Pattern Agent node. This agent generates a candlestick chart image and uses a vision LLM to identify classical price patterns.Parameters
ParameterTypeDescription
agent_llmBaseChatModelUsed for the tool-calling step to invoke generate_kline_image.
graph_llmBaseChatModelVision-capable LLM used to analyze the generated chart image.
toolkitTechnicalToolsToolkit instance exposing generate_kline_image.
Tool used
  • generate_kline_image — Generates a candlestick chart of the most recent 40 candles and returns a base64-encoded PNG.
Execution model
1

Check for precomputed image

If pattern_image is already present in the state, the tool-calling step is skipped.
2

Generate chart

agent_llm is prompted to call generate_kline_image with the current kline_data.
3

Vision analysis

graph_llm receives the base64 image alongside a list of 16 classical candlestick pattern definitions. The LLM identifies the best matching pattern and explains its reasoning.
Failed LLM or tool calls are automatically retried up to 3 times with an 8-second wait.Recognized patterns include: Inverse Head and Shoulders, Double Bottom, Rounded Bottom, Hidden Base, Falling/Rising Wedge, Ascending/Descending Triangle, Bullish/Bearish Flag, Rectangle, Island Reversal, V-shaped Reversal, Rounded Top, Expanding Triangle, Symmetrical Triangle.State output
KeyTypeDescription
pattern_reportstrNarrative identifying the detected candlestick pattern(s) and reasoning.
messageslistUpdated message list.
from trend_agent import create_trend_agent
Creates the Trend Agent node. This agent generates a trendline-annotated chart and uses a vision LLM to assess the prevailing market direction.Parameters
ParameterTypeDescription
agent_llmBaseChatModelUsed for the tool-calling step to invoke generate_trend_image.
graph_llmBaseChatModelVision-capable LLM used to analyze the annotated chart.
toolkitTechnicalToolsToolkit instance exposing generate_trend_image.
Tool used
  • generate_trend_image — Generates a candlestick chart overlaid with fitted support (blue) and resistance (red) trendlines derived from recent closing prices, highs, and lows.
Execution model
1

Check for precomputed image

If trend_image is already present in the state, the tool-calling step is skipped.
2

Generate annotated chart

agent_llm is prompted to call generate_trend_image with the current kline_data.
3

Vision analysis

graph_llm analyzes how price interacts with the trendlines—bouncing, breaking through, or compressing—and predicts the short-term direction (upward, downward, or sideways).
Failed calls are retried up to 3 times with a 4-second wait.State output
KeyTypeDescription
trend_reportstrNarrative describing trendline interaction and directional prediction.
trend_imagestrBase64-encoded PNG of the annotated chart.
trend_image_filenamestrLocal filename where the chart was saved ("trend_graph.png").
trend_image_descriptionstrShort description of the chart.
messageslistUpdated message list.
from decision_agent import create_final_trade_decider
Creates the Decision Maker node. This agent synthesizes the three upstream reports and issues an actionable LONG or SHORT trade directive.Parameters
ParameterTypeDescription
llmBaseChatModelThe LLM used to produce the final decision. Does not use tools.
Decision logicThe LLM is instructed to:
  1. Prioritize decisions where all three reports (Indicator, Pattern, Trend) align in the same direction.
  2. Give higher weight to strong, confirmed signals — MACD crossovers, RSI divergence, breakout confirmation.
  3. Default to the dominant trendline slope when reports are mixed (e.g., SHORT in a descending channel).
  4. Always output either LONG or SHORT. HOLD is prohibited in the HFT context.
  5. Suggest a risk-reward ratio between 1.2 and 1.8.
Decision JSON output formatThe final_trade_decision field contains a JSON string with the following structure:
{
  "forecast_horizon": "Predicting next 3 candlesticks (45 minutes)",
  "decision": "LONG",
  "justification": "MACD bullish crossover confirmed by ascending support trendline bounce. RSI recovering from 38. Bullish flag pattern approaching breakout.",
  "risk_reward_ratio": "1.5"
}
​
forecast_horizon
string
Human-readable description of the prediction window (e.g., "Predicting next 3 candlesticks (45 minutes)").
​
decision
string
Trade direction: "LONG" or "SHORT".
​
justification
string
Concise reasoning grounded in the three upstream reports.
​
risk_reward_ratio
string
Float between 1.2 and 1.8 as a string (e.g., "1.5").
State output
KeyTypeDescription
final_trade_decisionstrJSON string containing the trade decision (see format above).
decision_promptstrThe full prompt string sent to the LLM, useful for debugging.
messageslistList containing the LLM response message.

​
Graph execution order

The agents are chained in this fixed sequence inside SetGraph.set_graph(): Each agent reads from the shared IndicatorAgentState and writes its outputs back into it. The Decision Maker has access to all three reports when it runs.

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