feat: Add Trade Strategist node + SQLite checkpointing + .gitignore restore
- Add Trade Strategist agent (trade_strategist_node.py): generates 5 actionable trade setups (Entry, SL, TP, R:R, Win%) after Portfolio Manager - Wire Trade Strategist into LangGraph pipeline (setup.py) - Add 'trade_possibilities' field to AgentState schema - Initialize trade_possibilities in Propagator initial state - Integrate SqliteSaver (langgraph-checkpoint-sqlite) for node-by-node state persistence to trading_agents_state.sqlite - Add unique thread_id (ticker_date) to graph config for checkpoint isolation - Update CLI (main.py) to display Trade Strategist progress + save report section - Restore full standard Python .gitignore (200+ rules) stripped in prior PR - Fix: convert trade_strategist_node to factory function (create_trade_strategist) to resolve LangGraph TypeError on missing 'llm' argument - Fix: use sqlite3.connect() directly instead of SqliteSaver.from_conn_string() to avoid _GeneratorContextManager TypeError
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ElMoorish
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venv/
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# ============================
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# TradingAgents Custom Ignores
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# ============================
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# API Keys / Secrets
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.env
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__pycache__/
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*.egg-info/
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.envrc
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# Virtual environments
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venv/
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.venv
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env/
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ENV/
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env.bak/
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venv.bak/
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# Reports and results (generated output)
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reports/
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results/
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eval_results/
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# SQLite state database
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*.sqlite
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# ============================
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# Standard Python Ignores
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# ============================
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__pycache__/
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*.py[cod]
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build/
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# pdm
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.idea/
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# Visual Studio Code
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.vscode/
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# macOS
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.DS_Store
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.LSOverride
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# Windows
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Thumbs.db
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ehthumbs.db
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Desktop.ini
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$RECYCLE.BIN/
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# ============================
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# Cache / Data
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# ============================
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**/data_cache/
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.streamlit/secrets.toml
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19
cli/main.py
19
cli/main.py
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@ -46,7 +46,7 @@ class MessageBuffer:
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"Research Team": ["Bull Researcher", "Bear Researcher", "Research Manager"],
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"Trading Team": ["Trader"],
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"Risk Management": ["Aggressive Analyst", "Neutral Analyst", "Conservative Analyst"],
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"Portfolio Management": ["Portfolio Manager"],
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"Portfolio Management": ["Portfolio Manager", "Trade Strategist"],
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}
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# Analyst name mapping
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@ -68,6 +68,7 @@ class MessageBuffer:
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"investment_plan": (None, "Research Manager"),
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"trader_investment_plan": (None, "Trader"),
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"final_trade_decision": (None, "Portfolio Manager"),
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"trade_possibilities": (None, "Trade Strategist"),
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}
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def __init__(self, max_length=100):
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@ -1046,8 +1047,11 @@ def run_analysis():
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selections["ticker"], selections["analysis_date"]
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)
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# Pass callbacks to graph config for tool execution tracking
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# (LLM tracking is handled separately via LLM constructor)
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args = graph.propagator.get_graph_args(callbacks=[stats_handler])
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args = graph.propagator.get_graph_args(
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selections["ticker"],
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selections["analysis_date"],
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callbacks=[stats_handler]
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)
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# Stream the analysis
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trace = []
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@ -1148,6 +1152,15 @@ def run_analysis():
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message_buffer.update_agent_status("Conservative Analyst", "completed")
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message_buffer.update_agent_status("Neutral Analyst", "completed")
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message_buffer.update_agent_status("Portfolio Manager", "completed")
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message_buffer.update_agent_status("Trade Strategist", "in_progress")
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# Trade Strategist
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if chunk.get("trade_possibilities"):
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message_buffer.update_report_section(
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"trade_possibilities", chunk["trade_possibilities"]
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)
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if message_buffer.agent_status.get("Trade Strategist") != "completed":
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message_buffer.update_agent_status("Trade Strategist", "completed")
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# Update the display
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update_display(layout, stats_handler=stats_handler, start_time=start_time)
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@ -0,0 +1,55 @@
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from langchain_core.prompts import ChatPromptTemplate
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from tradingagents.agents.utils.agent_states import AgentState
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def create_trade_strategist(llm):
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def trade_strategist_node(state: AgentState):
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"""
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Agent that analyzes the final trade decision and outputs 5 distinct trade setups.
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"""
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"""You are an elite Trade Strategist at a premier quantitative hedge fund.
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Your job is to take the final consensus decision from the Portfolio Manager and the Trader's investment plan, and synthesize them into exactly 5 specific, actionable trade setups.
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For the given asset, you must provide exactly 5 trade possibilities with the following parameters explicitly defined for each:
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- Trade Direction (Long/Short, Options, etc.)
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- Entry Price / Condition (e.g., Buy at market, Limit buy at $X, Wait for breakout above $X)
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- Stop Loss (SL) (Specific price level)
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- Take Profit (TP) (Specific price level)
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- Risk/Reward Ratio
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- Estimated Win Percentage (Probability of success based on current technicals/fundamentals, e.g., 65%)
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- Brief Rationale (1-2 sentences explaining why this setup makes sense)
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Format your output as a clean, highly readable Markdown document.
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Do not output anything besides the 5 trades and a brief introductory/concluding sentence.
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Use bullet points and bold text for the parameters so they are easily scannable."""
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),
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(
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"human",
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"""Asset: {company}
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Portfolio Manager's Final Decision:
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{final_decision}
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Trader's Investment Plan:
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{trader_plan}
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Please formulate the 5 Trade Possibilities based on the above data."""
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),
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]
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)
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chain = prompt | llm
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result = chain.invoke({
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"company": state.get("company_of_interest", ""),
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"final_decision": state.get("final_trade_decision", ""),
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"trader_plan": state.get("trader_investment_plan", "")
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})
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return {"trade_possibilities": result.content}
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return trade_strategist_node
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@ -74,3 +74,6 @@ class AgentState(MessagesState):
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RiskDebateState, "Current state of the debate on evaluating risk"
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]
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final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"]
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# final strategies
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trade_possibilities: Annotated[str, "5 Trade Possibilities with percentages, SL/TP"]
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@ -11,7 +11,7 @@ from tradingagents.agents.utils.agent_states import (
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class Propagator:
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"""Handles state initialization and propagation through the graph."""
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def __init__(self, max_recur_limit=100):
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def __init__(self, max_recur_limit=1000):
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"""Initialize with configuration parameters."""
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self.max_recur_limit = max_recur_limit
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@ -51,16 +51,23 @@ class Propagator:
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"fundamentals_report": "",
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"sentiment_report": "",
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"news_report": "",
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"trade_possibilities": "",
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}
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def get_graph_args(self, callbacks: Optional[List] = None) -> Dict[str, Any]:
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def get_graph_args(self, company_name: str, trade_date: str, callbacks: Optional[List] = None) -> Dict[str, Any]:
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"""Get arguments for the graph invocation.
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Args:
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company_name: The ticker being analyzed
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trade_date: The date of analysis
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callbacks: Optional list of callback handlers for tool execution tracking.
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Note: LLM callbacks are handled separately via LLM constructor.
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"""
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config = {"recursion_limit": self.max_recur_limit}
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thread_id = f"{company_name}_{trade_date}"
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config = {
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"recursion_limit": self.max_recur_limit,
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"configurable": {"thread_id": thread_id}
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}
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if callbacks:
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config["callbacks"] = callbacks
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return {
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from typing import Dict, Any
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from langchain_openai import ChatOpenAI
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from langgraph.graph import END, StateGraph, START
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from langgraph.prebuilt import ToolNode
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import sqlite3
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from langgraph.checkpoint.sqlite import SqliteSaver
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from tradingagents.agents import *
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from tradingagents.agents.utils.agent_states import AgentState
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from tradingagents.agents.trade_strategist_node import create_trade_strategist
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from .conditional_logic import ConditionalLogic
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@ -104,6 +108,7 @@ class GraphSetup:
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portfolio_manager_node = create_portfolio_manager(
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self.deep_thinking_llm, self.portfolio_manager_memory
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)
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trade_strategist_node = create_trade_strategist(self.quick_thinking_llm)
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# Create workflow
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workflow = StateGraph(AgentState)
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@ -125,6 +130,7 @@ class GraphSetup:
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workflow.add_node("Neutral Analyst", neutral_analyst)
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workflow.add_node("Conservative Analyst", conservative_analyst)
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workflow.add_node("Portfolio Manager", portfolio_manager_node)
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workflow.add_node("Trade Strategist", trade_strategist_node)
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# Define edges
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# Start with the first analyst
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@ -196,7 +202,10 @@ class GraphSetup:
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},
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)
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workflow.add_edge("Portfolio Manager", END)
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workflow.add_edge("Portfolio Manager", "Trade Strategist")
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workflow.add_edge("Trade Strategist", END)
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# Compile and return
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return workflow.compile()
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# Compile and return with SQLite memory
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conn = sqlite3.connect("trading_agents_state.sqlite", check_same_thread=False)
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memory = SqliteSaver(conn)
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return workflow.compile(checkpointer=memory)
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@ -200,7 +200,7 @@ class TradingAgentsGraph:
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init_agent_state = self.propagator.create_initial_state(
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company_name, trade_date
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)
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args = self.propagator.get_graph_args()
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args = self.propagator.get_graph_args(company_name, trade_date)
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if self.debug:
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# Debug mode with tracing
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@ -256,6 +256,7 @@ class TradingAgentsGraph:
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},
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"investment_plan": final_state["investment_plan"],
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"final_trade_decision": final_state["final_trade_decision"],
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"trade_possibilities": final_state.get("trade_possibilities", ""),
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}
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# Save to file
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