Documentation Index
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All four agents in QuantAgent share a single state dictionary typed as IndicatorAgentState. This TypedDict is defined in agent_state.py and is used as the schema for the LangGraph StateGraph.
Each agent reads fields it needs from the state and writes its outputs back into it. The state is initialized by you when calling graph.invoke(initial_state) and is fully populated by the time the graph reaches END.
IndicatorAgentState
from agent_state import IndicatorAgentState
initial_state before invoking the graph.
| Field | Type | Description |
|---|
kline_data | dict | OHLCV dictionary with keys Datetime, Open, High, Low, Close (lists of values). Used as input by all agents. |
time_frame | str | Candlestick interval string (e.g., "15min", "4hour", "1d"). Embedded in agent prompts. |
stock_name | str | Ticker or asset name (e.g., "BTC", "AAPL"). Embedded in the Decision agent prompt. |
analysis_results | str | None | Pass None on the first invocation. |
messages | List[BaseMessage] | LangChain message history. Pass [] to start fresh. |
Indicator agent outputs
Populated by create_indicator_agent after it runs.
| Field | Type | Description |
|---|
rsi | List[float] | Relative Strength Index values (last 28). |
macd | List[float] | MACD line values (full series). |
macd_signal | List[float] | MACD signal line values (last 28). |
macd_hist | List[float] | MACD histogram values (last 28). |
stoch_k | List[float] | Stochastic Oscillator slow %K values (last 28). |
stoch_d | List[float] | Stochastic Oscillator slow %D values (last 28). |
roc | List[float] | Rate of Change values (last 28). |
willr | List[float] | Williams %R values (last 28). |
indicator_report | str | Narrative summary of all computed indicators. Passed to the Decision agent. |
Pattern agent outputs
Populated by create_pattern_agent after it runs.
| Field | Type | Description |
|---|
pattern_image | str | Base64-encoded PNG of the candlestick chart. Can be pre-populated to skip tool generation. |
pattern_image_filename | str | Local file path of the saved chart image ("kline_chart.png"). |
pattern_image_description | str | Short description of the chart image. |
pattern_report | str | Narrative identifying detected candlestick patterns. Passed to the Decision agent. |
Trend agent outputs
Populated by create_trend_agent after it runs.
| Field | Type | Description |
|---|
trend_image | str | Base64-encoded PNG of the trendline-annotated chart. Can be pre-populated to skip tool generation. |
trend_image_filename | str | Local file path of the saved chart ("trend_graph.png"). |
trend_image_description | str | Description including support/resistance line characteristics. |
trend_report | str | Narrative describing trendline interaction and short-term directional prediction. Passed to the Decision agent. |
Decision agent outputs
Populated by create_final_trade_decider after it runs.
| Field | Type | Description |
|---|
final_trade_decision | str | JSON string with keys forecast_horizon, decision (LONG/SHORT), justification, risk_reward_ratio. |
decision_prompt | str | The full prompt string sent to the LLM. Useful for debugging. |
Constructing initial_state
The minimal initial_state you need to pass to graph.invoke():
import pandas as pd
# Prepare kline_data from a DataFrame
df = pd.read_csv("btc_15min.csv")
kline_data = df[["Datetime", "Open", "High", "Low", "Close"]].to_dict(orient="list")
initial_state = {
# Required inputs
"kline_data": kline_data,
"time_frame": "15min",
"stock_name": "BTC-USD",
# Required placeholders
"analysis_results": None,
"messages": [],
}
You can pre-populate pattern_image or trend_image in initial_state to skip the chart generation tool call in those agents. This is useful when you have already generated a chart externally or want to reuse a chart from a previous run.
Reading the final state
After graph.invoke() returns, the state dict contains all populated fields:
import json
final_state = tg.graph.invoke(initial_state)
# Parse the trade decision
decision = json.loads(final_state["final_trade_decision"])
print(decision["decision"]) # "LONG" or "SHORT"
print(decision["justification"])
print(decision["risk_reward_ratio"])
# Read individual agent reports
print(final_state["indicator_report"])
print(final_state["pattern_report"])
print(final_state["trend_report"])
# Access raw indicator values
print(final_state["rsi"])
print(final_state["macd_hist"])
Type definition
from typing import Annotated, List, TypedDict
from langchain_core.messages import BaseMessage
class IndicatorAgentState(TypedDict):
# Inputs
kline_data: Annotated[dict, "OHLCV dictionary used for computing technical indicators"]
time_frame: Annotated[str, "time period for k line data provided"]
stock_name: Annotated[dict, "stock name for prompt"]
# Indicator agent
rsi: Annotated[List[float], "Relative Strength Index values"]
macd: Annotated[List[float], "MACD line values"]
macd_signal: Annotated[List[float], "MACD signal line values"]
macd_hist: Annotated[List[float], "MACD histogram values"]
stoch_k: Annotated[List[float], "Stochastic Oscillator %K values"]
stoch_d: Annotated[List[float], "Stochastic Oscillator %D values"]
roc: Annotated[List[float], "Rate of Change values"]
willr: Annotated[List[float], "Williams %R values"]
indicator_report: Annotated[str, "Final indicator agent summary report"]
# Pattern agent
pattern_image: Annotated[str, "Base64-encoded K-line chart"]
pattern_image_filename: Annotated[str, "Local file path to saved K-line chart image"]
pattern_image_description: Annotated[str, "Brief description of the generated K-line image"]
pattern_report: Annotated[str, "Final pattern agent summary report"]
# Trend agent
trend_image: Annotated[str, "Base64-encoded trend-annotated candlestick chart"]
trend_image_filename: Annotated[str, "Local file path to saved trendline-enhanced chart"]
trend_image_description: Annotated[str, "Description including support/resistance characteristics"]
trend_report: Annotated[str, "Final trend analysis summary"]
# Decision agent
analysis_results: Annotated[str, "Computed result of the analysis or decision"]
messages: Annotated[List[BaseMessage], "List of chat messages used in LLM prompt construction"]
decision_prompt: Annotated[str, "Decision prompt for reflection"]
final_trade_decision: Annotated[str, "Final LONG or SHORT decision JSON"]