mirror of
https://github.com/farcasclaudiu/TradingAgents.git
synced 2026-06-22 05:01:20 +03:00
Yijia-Xiao
4cbd4b086f
Long analyses can take many minutes; a crash or interruption forced users to re-run from scratch and re-pay every LLM call. This adds an opt-in checkpoint layer backed by per-ticker SQLite databases so the graph resumes from the last successful node. How to use: - CLI: tradingagents analyze --checkpoint - CLI: tradingagents analyze --clear-checkpoints - Python: config["checkpoint_enabled"] = True Lifecycle: - propagate() recompiles the graph with a SqliteSaver when enabled and injects a deterministic thread_id derived from ticker+date so the same ticker+date resumes while a different date starts fresh. - On successful completion the per-thread checkpoint rows are cleared. - The context manager is closed in a try/finally so a crash never leaks the SQLite connection or leaves the graph in checkpoint mode. Storage: ~/.tradingagents/cache/checkpoints/<TICKER>.db (override via TRADINGAGENTS_CACHE_DIR). The checkpointer module is new (tradingagents/graph/checkpointer.py) and the GraphSetup now returns the uncompiled workflow so it can be recompiled with a saver when needed. Adds langgraph-checkpoint-sqlite>=2.0.0 dependency. 3 new tests verify the crash/resume cycle and that a different date starts fresh.
42 lines
2.0 KiB
Python
42 lines
2.0 KiB
Python
import os
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_TRADINGAGENTS_HOME = os.path.join(os.path.expanduser("~"), ".tradingagents")
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DEFAULT_CONFIG = {
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"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
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"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", os.path.join(_TRADINGAGENTS_HOME, "logs")),
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"data_cache_dir": os.getenv("TRADINGAGENTS_CACHE_DIR", os.path.join(_TRADINGAGENTS_HOME, "cache")),
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"memory_log_path": os.getenv("TRADINGAGENTS_MEMORY_LOG_PATH", os.path.join(_TRADINGAGENTS_HOME, "memory", "trading_memory.md")),
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# LLM settings
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"llm_provider": "openai",
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"deep_think_llm": "gpt-5.4",
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"quick_think_llm": "gpt-5.4-mini",
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"backend_url": "https://api.openai.com/v1",
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# Provider-specific thinking configuration
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"google_thinking_level": None, # "high", "minimal", etc.
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"openai_reasoning_effort": None, # "medium", "high", "low"
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"anthropic_effort": None, # "high", "medium", "low"
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# Checkpoint/resume: when True, LangGraph saves state after each node
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# so a crashed run can resume from the last successful step.
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"checkpoint_enabled": False,
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# Output language for analyst reports and final decision
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# Internal agent debate stays in English for reasoning quality
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"output_language": "English",
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# Debate and discussion settings
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"max_debate_rounds": 1,
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"max_risk_discuss_rounds": 1,
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"max_recur_limit": 100,
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# Data vendor configuration
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# Category-level configuration (default for all tools in category)
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"data_vendors": {
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"core_stock_apis": "yfinance", # Options: alpha_vantage, yfinance
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"technical_indicators": "yfinance", # Options: alpha_vantage, yfinance
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"fundamental_data": "yfinance", # Options: alpha_vantage, yfinance
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"news_data": "yfinance", # Options: alpha_vantage, yfinance
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},
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# Tool-level configuration (takes precedence over category-level)
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"tool_vendors": {
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# Example: "get_stock_data": "alpha_vantage", # Override category default
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},
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}
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