| coaching_start_coachingA | 启动学习辅导，分析投资问题。
自动判断问题是简单还是复杂：
- 简单问题（如"该不该买XX"）→ 直接输出 10 步标准投研分析
- 复杂问题（如"为什么XX和YY走势背离"）→ 启动 Socratic 多轮引导对话
Args:
user_input: 用户提出的投资问题（如"茅台2000块值得买吗"）
mode: 强制模式，"simple" 或 "complex"，留空则自动判断
Returns:
simple 模式: {"status":"success", "mode":"simple", "answer": "...", "scenario_id":"..."}
complex 模式: {"status":"success", "mode":"complex", "session_id":"...", "question":"...", "options":[...]}
|
| coaching_continue_coachingA | 继续一个进行中的 Socratic 多轮辅导会话。
回复选项字母 (A/B/C/D) 来推进对话，或输入
「直接说」切换到简单模式、「别问了」放弃本轮。
Args:
session_id: 由 start_coaching 返回的会话 ID
user_input: 用户对当前问题的回答（选项字母或指令文本）
Returns:
dict: 下一轮问题或最终结果
|
| coaching_abandon_coachingB | 放弃当前进行中的 Socratic 辅导会话并归档。
Args:
session_id: 要放弃的会话 ID
Returns:
dict: 包含 final_summary 的归档结果
|
| coaching_list_coaching_scenariosB | 列出最近的学习辅导情景记录。
每次辅导结束（无论是简单模式还是复杂模式）都会自动归档到情景库，
包括触发事件、变量结构、因果链和最终结论。
Args:
limit: 返回的记录数量，默认 20
Returns:
dict: {"count": N, "scenarios": [...]}
|
| coaching_coaching_switch_to_simpleA | 将进行中的 Socratic 多轮辅导会话切换到简单模式。
放弃剩余的问答题，直接用已有的分析上下文走一遍
10 步标准投研模板输出，并正常归档到情景库。
Args:
session_id: 由 start_coaching 返回的会话 ID
Returns:
dict: simple 模式结果，含 answer 和 scenario_id
|
| coaching_coaching_compare_historyA | 对比当前新事件与历史情景的变量结构异同。
从情景库中取出指定历史事件，用 LLM 对比分析：
- 相同点是什么
- 核心差异是什么
- 上次的教训是否适用于本次
Args:
event: 当前新事件的描述（如"美联储降息50bp"）
scenario_id: 要对比的历史情景 ID（从 list_coaching_scenarios 获取）
Returns:
dict: {"status":"success", "comparison": "..."}
|
| coaching_coaching_suggest_thresholdsB | 根据用户过去的交易记录，建议触发深度分析的数值阈值。
对于指定的资产类别（如"利率"、"汇率"、"商品"），
LLM 会分析多大的变动幅度应该触发深度分析，
给出具体数值阈值建议并说明理由。
Args:
asset_class: 资产类别，如"利率"、"汇率"、"商品"、"A股"、"美股"
Returns:
dict: {"status":"success", "suggestion": "..."}
|
| coaching_coaching_iterate_judgmentA | 当用户判断错误后，更新投研规则中的变量优先级。
输入上次判断错误的变量和实际正确结果，
LLM 会分析并给出变量优先级调整建议——下次遇到类似结构，
哪个变量该调高、哪个该调低。
Args:
wrong_variable: 未按预期变化的变量（如"黄金与美元同涨"）
correct_result: 实际发生的正确结果（如"黄金独立上涨，美元回落"）
Returns:
dict: {"status":"success", "iteration": "..."}
|
| thought_record_thoughtB | Record an investment thought/idea from the user.
Args:
text: The raw thought text from user (e.g., "茅台跌到1400可以考虑买入")
Returns:
dict: A thought card with parsed entities and card ID
|
| thought_search_memoriesB | Search user's historical thoughts and memories using semantic search.
Args:
query: Search query (e.g., "茅台", "上次关于苹果的想法")
Returns:
list: Matching thought cards, sorted by relevance
|
| thought_get_thought_cardsC | Get all thought cards for a specific ticker/symbol.
Args:
ticker: Stock ticker (e.g., "贵州茅台", "AAPL")
Returns:
list: All thought cards for this ticker
|
| rag_ask_investmentB | Ask an investment question and get RAG-augmented answer.
Args:
question: Investment question (e.g., "什么是护城河？")
Returns:
dict: Answer with source citations from master knowledge base
|
| rag_get_master_viewA | Get a specific master's view on a topic.
Args:
master: Master name (e.g., "巴菲特", "芒格", "段永平")
topic: Topic to query (e.g., "护城河", "安全边际")
Returns:
dict: Master's view on the topic with citations
|
| rag_search_knowledgeC | Search the investment knowledge base.
Args:
query: Search query (e.g., "价值投资", "周期")
Returns:
list: Matching knowledge entries
|
| rag_rag_search_reportsB | 搜索券商研报——基于语义向量检索。 返回最近入库的券商研报摘要，包含机构、股票、行业、评级、盈利预测等信息。
Args:
query: 搜索关键词（如"宁德时代 业绩"、"保险 增长"、"半导体"）
top_k: 返回结果数量，默认5
Returns:
{
"query": str,
"count": int,
"results": [
{
"info_code": str,
"title": str,
"org_name": str,
"stock_name": str,
"stock_code": str,
"industry_name": str,
"rating": str,
"rating_change": str,
"publish_date": str,
"text": str,
"distance": float,
}
]
}
|
| memory_get_user_profileB | Get user investment profile.
Returns:
dict: User profile including risk preference, circle of competence, investment principles
|
| memory_record_decisionB | Record an investment decision made by the user.
Args:
data: Decision data including ticker, action (buy/sell/hold), price, reason, timestamp
Returns:
dict: Confirmation of recorded decision
|
| memory_get_behavior_patternsB | Get identified behavior patterns from user's investment history.
Returns:
list: Behavior patterns like "tends to buy on price drops", "stops loss too early"
|
| reminder_set_reminderA | Set a reminder with trigger condition.
Args:
condition: Reminder condition, e.g.:
- {"type": "price", "ticker": "贵州茅台", "operator": "below", "value": 1400}
- {"type": "time", "cron": "0 9 * * 5"} # Every Friday 9am
- {"type": "pe_ratio", "ticker": "贵州茅台", "operator": "below", "value": 20}
Returns:
dict: Created reminder with ID
|
| reminder_get_remindersB | Get all active reminders.
Returns:
list: All active reminders
|
| reminder_delete_reminderC | Delete a reminder by ID.
Args:
id: Reminder ID to delete
Returns:
dict: Deletion confirmation
|
| pattern_run_pattern_detectionB | Run behavior pattern detection on all recorded decisions and thoughts.
Detects: 追高, 过早卖出, 情绪化交易, 原则违反.
Results are saved to the behavior_patterns table.
Returns:
dict: Detection results with count and pattern list
|
| pattern_get_pattern_summaryA | Get cumulative summary of all detected behavior patterns.
Returns:
dict: Pattern type -> {count, last_seen}, plus prompt_hint for
the agent to decide whether to proactively engage the user
|
| pattern_get_pattern_reportB | Generate a behavior pattern report in Markdown format.
Args:
period: Report period — "weekly" (default) or "monthly"
Returns:
dict: Report path and full Markdown content
|
| report_run_scheduled_reportA | Generate a behavior pattern report for this week or month.
Designed to be called on a schedule by the agent (e.g. every Sunday).
Args:
range: Report range — "week" (default) or "month"
Returns:
dict: Compact report with summary, insights, highlight, and report file path
|
| invest_invest_roundtableA | 投资大师圆桌讨论。
自动语义匹配 3-5 位最相关的投资大师，模拟结构化深度对话。
每位大师独立调用 LLM 生成观点，主持人综合交锋并生成结构化纪要。
Args:
topic: 投资议题（如 "贵州茅台当前估值合理吗？"）
Returns:
dict: 含 scene / masters / rounds / verdict 的结构化圆桌纪要
|
| notify_configure_notifierB | Configure a notification channel.
Args:
channel: Notification channel name. Supported: feishu, dingtalk, bark, pushplus
webhook_url: Webhook URL for webhook-based channels (feishu / dingtalk / bark)
token: PushPlus token (only used when channel="pushplus")
enabled: Whether to enable this channel (default True)
send_test: Whether to immediately send a test message (default False)
Returns:
dict: Current configuration status for all channels, plus test result if requested
|
| notify_get_notifier_configA | Get the current notification configuration for all channels.
Returns:
dict: Configuration status for all channels, e.g.:
{"channels": {"feishu": {...}, "dingtalk": {...}, "bark": {...}, "pushplus": {...}}}
|
| market_get_stock_quoteB | Get real-time quote for a stock ticker.
Args:
ticker: Stock ticker symbol (e.g., "600519", "sh600519", "贵州茅台")
Returns:
dict: Current price, change %, volume
|
| market_get_stock_historyA | Get historical K-line data for a stock.
Args:
ticker: Stock ticker symbol (e.g., "600519")
period: K-line period - "daily", "weekly", "monthly" (default: "daily")
Returns:
dict: OHLCV data with date, open, high, low, close, volume
|
| market_get_index_componentsC | Get constituent stocks of an index (CSI300, etc).
Note: Simplified - returns basic info from Sina.
Args:
index_code: Index code (e.g., "000300" for CSI 300, "000001" for Shanghai)
Returns:
dict: Index name and current value
|
| market_get_valuationB | Get valuation data for a stock.
Note: Sina provides limited fundamental data - returns price-based metrics.
Args:
ticker: Stock ticker symbol (e.g., "600519")
Returns:
dict: Price-based valuation info
|
| market_get_market_sentimentB | Get overall market sentiment indicators.
Returns Shanghai Composite as a proxy for market sentiment.
Returns:
dict: Index value, change %, sentiment reading
|
| tushare_get_daily_priceA | Get daily price data for a stock (Tushare API: daily).
Args:
ts_code: Tushare stock code (e.g., "600519.SH" for 贵州茅台)
Format: {code}.{exchange} (SH=Shanghai, SZ=Shenzhen)
start_date: Start date in YYYYMMDD format (default: 30 days ago)
end_date: End date in YYYYMMDD format (default: today)
Returns:
dict: OHLCV data with date, open, high, low, close, volume, amount
|
| tushare_get_weekly_priceB | Get weekly price data for a stock (Tushare API: weekly).
Args:
ts_code: Tushare stock code (e.g., "600519.SH")
start_date: Start date in YYYYMMDD format
end_date: End date in YYYYMMDD format
Returns:
dict: Weekly OHLCV data
|
| tushare_get_realtime_quoteC | Get realtime quote for a stock (Tushare API:实时行情).
Args:
ts_code: Tushare stock code (e.g., "600519.SH")
Returns:
dict: Current price, change %, volume, etc.
|
| tushare_get_index_dailyC | Get index daily K-line data (Tushare API: index_daily).
Args:
index_code: Index code (e.g., "000300.SH" for CSI 300, "000001.SH" for Shanghai)
start_date: Start date in YYYYMMDD format
end_date: End date in YYYYMMDD format
Returns:
dict: Index OHLCV data
|
| tushare_get_financial_indicatorC | Get financial indicators for a stock (Tushare API: fina_indicator).
Includes: PE, PB, ROE, gross margin, etc.
Args:
ts_code: Tushare stock code (e.g., "600519.SH")
Returns:
dict: Financial indicators
|
| tushare_get_income_statementC | Get income statement (P&L) for a stock (Tushare API: income).
Args:
ts_code: Tushare stock code (e.g., "600519.SH")
period: Reporting period in YYYYMMDD format (e.g., "20250331" for Q1 2025)
Returns:
dict: Income statement data
|
| tushare_get_balance_sheetB | Get balance sheet data (Tushare API: balancesheet).
Args:
ts_code: Tushare stock code (e.g., "600519.SH")
period: Reporting period in YYYYMMDD format
Returns:
dict: Balance sheet data
|
| tushare_get_cash_flowB | Get cash flow statement (Tushare API: cashflow).
Args:
ts_code: Tushare stock code (e.g., "600519.SH")
period: Reporting period in YYYYMMDD format
Returns:
dict: Cash flow data
|
| tushare_get_index_componentsB | Get constituent stocks of an index (Tushare API: index_weight).
Args:
index_code: Index code (e.g., "000300.SH" for CSI 300)
Returns:
dict: List of constituent stocks with weight
|
| tushare_get_industry_classificationC | Get industry classification for a stock (Tushare API: stock_basic + industry).
Args:
ts_code: Tushare stock code (e.g., "600519.SH")
Returns:
dict: Industry info (SW industry, Shenwan classification)
|
| tushare_get_valuation_multiB | Get PE/PB ratios for multiple stocks over time (Tushare API: daily + basic).
Args:
stock_list: List of Tushare stock codes (e.g., ["600519.SH", "000858.SZ"])
start_date: Start date in YYYYMMDD format
end_date: End date in YYYYMMDD format
Returns:
dict: Valuation data for multiple stocks
|
| tushare_get_market_top_moversB | Get top gainers/losers for the day (Tushare API: top_list).
Args:
trade_date: Trading date in YYYYMMDD format (default: today)
Returns:
dict: Top gainers, losers, and most active stocks
|
| tushare_get_stock_pledge_statusB | Get stock pledge (股权质押) status (Tushare API: pledge_stat).
Args:
ts_code: Tushare stock code (e.g., "600519.SH")
Returns:
dict: Pledge ratio and status
|
| tushare_convert_tickerB | Convert common stock ticker to Tushare format.
Args:
ts_code_or_ticker: Either Tushare format (600519.SH) or plain number (600519)
Returns:
str: Tushare format code
|
| tushare_check_token_statusA | Check if Tushare token is configured and valid.
Returns:
dict: Token status and user info
|
| user_knowledge_addB | 写入用户知识到知识库。 支持 4 类来源：framework（投资框架）、journal（交易日志）、
reading_notes（读书笔记）、insight（关键洞察，默认）。
Args:
title: 知识标题
content: 正文内容（将用于语义搜索的 embedding）
tags: 标签列表，如 ["framework:gold", "strategy:tactical"]
source_type: 来源类型，可选 framework / journal / reading_notes / insight
Returns:
{id, title, status: "added"}
|
| user_knowledge_updateA | 更新已有用户知识条目。 只传需要修改的字段，未传的字段保持不变。
Args:
id: 知识条目 ID（如 uk_1722000000）
content: 新正文（可选）
title: 新标题（可选）
tags: 新标签列表（可选）
Returns:
{id, status: "updated", version}
|
| user_knowledge_deleteB | 从知识库中删除指定知识条目。 Args:
id: 知识条目 ID（如 uk_1722000000）
Returns:
{id, status: "deleted"}
|
| user_knowledge_listA | 列出用户知识条目，可按来源类型或标签筛选。 Args:
source_type: 按来源类型过滤（framework / journal / reading_notes / insight）
tag: 按标签过滤（如 "framework:gold"）
limit: 返回条目数量上限，默认 50
Returns:
{count, items: [{id, title, tags, source_type, created_at}, ...]}
|
| user_knowledge_searchA | 语义搜索用户知识库。 基于向量相似度检索最相关的用户知识条目。
Args:
query: 搜索查询文本
top_k: 返回结果数量，默认 5
Returns:
{query, results: [{id, title, snippet, tags, score, source_type, created_at}, ...]}
|
| user_create_profileA | 创建本地用户 profile。 首次使用前必须创建，用于留存/活跃度追踪的锚点。
Args:
nickname: 用户昵称
risk_profile: 风险偏好，可选 conservative / moderate / aggressive
investment_style: 投资风格，可选 value / growth / trend / mixed
experience_years: 投资经验年限
goals: 投资目标列表，如 ["长期复利", "跑赢沪深300"]
settings: 附加设置（JSON 对象），如 {"currency": "CNY", "notify_channel": "feishu"}
user_id: 用户 ID（默认 local 单用户）
Returns:
{created: true, profile: {...}}
|
| user_get_profileB | 读取本地用户 profile。 Args:
user_id: 用户 ID（默认 local）
Returns:
{exists: true/false, profile: {...}} — profile 含昵称/风险偏好/投资风格/
经验年限/目标/设置/创建与最近活跃时间
|
| user_update_profileB | 更新本地用户 profile（只更新传入字段）。 Args:
nickname / risk_profile / investment_style / experience_years / goals / settings: 需更新的字段
user_id: 用户 ID（默认 local）
Returns:
{updated: true/false, profile: {...}}
|
| user_record_activityA | 记录一次用户活跃事件（自动更新 last_active_at）。 供其他工具/Agent 在关键行为后调用，用于留存与活跃度统计。
Args:
activity_type: 事件类型，如 thought / decision / reminder / pattern / search / coaching / report
detail: 事件说明（可选，最多 500 字）
user_id: 用户 ID（默认 local）
Returns:
{recorded: true/false, activity_id}
|
| user_get_activity_statsA | 获取用户活跃度统计（近 N 天）。 Args:
user_id: 用户 ID（默认 local）
days: 统计窗口天数，默认 30
Returns:
{exists, stats: {total, active_days, daily_avg, by_type, last_activity_at, active_date_list}}
|
| user_get_retentionB | 获取用户留存摘要（首次/最近活跃、活跃天数、连续活跃天数、留存跨度）。 Args:
user_id: 用户 ID（默认 local）
Returns:
{exists, retention: {...}}
|
| broker_supported_formatsA | 查看 InvestBrain 支持的券商交割单格式与标准字段映射。 用于导入前确认券商格式兼容性；返回各券商典型列名与标准化字段对照。
Returns:
{brokers: [{id, name, sample_columns}], standard_fields: {...}, note}
|
| broker_import_statementA | 导入券商交割单 CSV 文件，解析为标准化交易记录并入库（自动去重）。 Args:
file_path: 券商导出的交割单/对账单 CSV 文件绝对路径
broker: 券商标识，可选 htsc / citic / cms / gtja / generic；
默认 auto 自动按表头特征识别（无法确定时按 generic 处理）
Returns:
{
import_id, broker, file_name,
total_rows, imported_rows, duplicated_rows, skipped_rows,
first_date, last_date, warnings, hint
}
重复导入同一文件时返回 imported_rows=0 并提示已存在。
|
| broker_list_tradesA | 查询已导入的标准化交易记录（默认时间倒序）。 Args:
start_date: 起始日期 YYYY-MM-DD（含）
end_date: 结束日期 YYYY-MM-DD（含）
symbol: 按证券代码模糊筛选（如 600519）
side: 买卖方向筛选，可选 buy / sell / other / unknown
broker: 券商标识筛选，可选 htsc / citic / cms / gtja / generic
limit: 返回条数上限（默认 200，最大 2000）
Returns:
{total: n, trades: [{id, trade_date, trade_time, side, symbol,
symbol_name, price, quantity, amount, fee,
net_amount, broker, contract_no}...]}
|
| broker_get_trade_statsA | 汇总已导入交易的统计指标（买卖笔数/股数/金额、费用、持仓标的数）。 Args:
start_date: 起始日期 YYYY-MM-DD（含）
end_date: 结束日期 YYYY-MM-DD（含）
broker: 券商标识筛选（默认全部）
Returns:
{buy: {...}, sell: {...}, total_trades, distinct_symbols, first_date, last_date}
|
| broker_get_importsB | 查看交割单导入历史（文件、券商、导入时间、成功/跳过行数）。 Args:
limit: 返回最近 N 次导入记录（默认 20，最大 200）
Returns:
{imports: [{id, broker, file_name, imported_rows, skipped_rows,
total_rows, first_date, last_date, created_at}...]}
|
