nofx/agent/agent.go

// Package agent implements the NOFXi Agent Core.
//
// Architecture: ALL user messages go to the LLM. The LLM understands intent
// and calls tools to execute actions. No regex routing, no pattern matching.
// The LLM IS the brain — just like how OpenClaw works.
package agent

import (
	"context"
	"encoding/json"
	"fmt"
	"log/slog"
	"net/http"
	"sort"
	"strconv"
	"strings"
	"time"

	"nofx/manager"
	"nofx/market"
	"nofx/mcp"
	"nofx/safe"
	"nofx/store"
)

type Agent struct {
	traderManager *manager.TraderManager
	store         *store.Store
	aiClient      mcp.AIClient
	config        *Config
	sentinel      *Sentinel
	brain         *Brain
	scheduler     *Scheduler
	logger        *slog.Logger
	history       *chatHistory
	pending       *pendingTrades
	NotifyFunc    func(userID int64, text string) error
}

type Config struct {
	Language       string   `json:"language"`
	WatchSymbols   []string `json:"watch_symbols"`
	EnableBriefs   bool     `json:"enable_briefs"`
	EnableNews     bool     `json:"enable_news"`
	EnableSentinel bool     `json:"enable_sentinel"`
	BriefTimes     []int    `json:"brief_times"`
}

func DefaultConfig() *Config {
	return &Config{
		Language: "zh", WatchSymbols: []string{"BTCUSDT", "ETHUSDT", "SOLUSDT"},
		EnableBriefs: true, EnableNews: true, EnableSentinel: true, BriefTimes: []int{8, 20},
	}
}

func New(tm *manager.TraderManager, st *store.Store, cfg *Config, logger *slog.Logger) *Agent {
	if cfg == nil { cfg = DefaultConfig() }
	return &Agent{traderManager: tm, store: st, config: cfg, logger: logger, history: newChatHistory(20), pending: newPendingTrades()}
}

func (a *Agent) SetAIClient(c mcp.AIClient) { a.aiClient = c }

func (a *Agent) EnsureAIClient() {
	if a.aiClient != nil { return }
	if a.store != nil {
		models, err := a.store.AIModel().List("default")
		if err == nil {
			for _, m := range models {
				apiKey := string(m.APIKey)
				if apiKey != "" && m.CustomAPIURL != "" {
					// Use standard HTTP client (no SSRF protection) since we control the URLs
					httpClient := &http.Client{Timeout: 60 * time.Second}
					client := mcp.NewClient(mcp.WithHTTPClient(httpClient))
					name := m.CustomModelName
					if name == "" { name = m.ID }
					client.SetAPIKey(apiKey, m.CustomAPIURL, name)
					a.aiClient = client
					a.logger.Info("agent AI client ready", "model", name)
					return
				}
			}
		}
	}
	a.logger.Warn("no AI client — agent will have limited capabilities")
}

func (a *Agent) Start() {
	a.logger.Info("starting NOFXi agent...")
	a.EnsureAIClient()

	if a.config.EnableSentinel {
		a.sentinel = NewSentinel(a.config.WatchSymbols, a.handleSignal, a.logger)
		a.sentinel.Start()
	}
	a.brain = NewBrain(a, a.logger)
	if a.config.EnableNews { a.brain.StartNewsScan(5 * time.Minute) }
	if a.config.EnableBriefs { a.brain.StartMarketBriefs(a.config.BriefTimes) }
	a.scheduler = NewScheduler(a, a.logger)
	a.scheduler.Start(context.Background())

	// Periodic cleanup of stale chat sessions (older than 4 hours)
	safe.GoNamed("chat-history-cleanup", func() {
		ticker := time.NewTicker(30 * time.Minute)
		defer ticker.Stop()
		for range ticker.C {
			a.history.CleanOld(4 * time.Hour)
		}
	})

	a.logger.Info("NOFXi agent is online 🚀")
}

func (a *Agent) Stop() {
	if a.sentinel != nil { a.sentinel.Stop() }
	if a.brain != nil { a.brain.Stop() }
	if a.scheduler != nil { a.scheduler.Stop() }
}

// HandleMessage — the core. Everything goes through the LLM.
func (a *Agent) HandleMessage(ctx context.Context, userID int64, text string) (string, error) {
	lang := a.config.Language
	if strings.HasPrefix(text, "[lang:") {
		if end := strings.Index(text, "] "); end > 0 {
			lang = text[6:end]; text = text[end+2:]
		}
	}

	a.logger.Info("message", "user_id", userID, "text", text)

	// Setup flow — only when user explicitly asks
	if resp, handled := a.handleSetupFlow(userID, text, lang); handled {
		return resp, nil
	}

	// Only handle bare slash commands directly (instant, no AI needed)
	if text == "/help" || text == "/start" {
		return a.msg(lang, "help"), nil
	}
	if text == "/status" {
		return a.handleStatus(lang), nil
	}
	if text == "/clear" {
		a.history.Clear(userID)
		if lang == "zh" {
			return "🧹 对话记忆已清除。", nil
		}
		return "🧹 Conversation history cleared.", nil
	}

	// Check for trade confirmation (e.g. "确认 trade_xxx" or "confirm trade_xxx")
	if resp, handled := a.handleTradeConfirmation(ctx, userID, text, lang); handled {
		return resp, nil
	}

	// Check for direct trade commands (e.g. "做多 BTC 0.01")
	if trade := parseTradeCommand(text); trade != nil {
		a.pending.Add(trade)
		a.pending.CleanExpired()
		return formatTradeConfirmation(trade, lang), nil
	}

	// EVERYTHING else → LLM with tools
	return a.thinkAndAct(ctx, userID, lang, text)
}

// HandleMessageStream is like HandleMessage but streams the final LLM response via SSE.
// onEvent is called with (eventType, data) — see StreamEvent* constants.
// Non-streamable responses (commands, trade confirmations) return immediately without events.
func (a *Agent) HandleMessageStream(ctx context.Context, userID int64, text string, onEvent func(event, data string)) (string, error) {
	lang := a.config.Language
	if strings.HasPrefix(text, "[lang:") {
		if end := strings.Index(text, "] "); end > 0 {
			lang = text[6:end]; text = text[end+2:]
		}
	}

	a.logger.Info("message (stream)", "user_id", userID, "text", text)

	if resp, handled := a.handleSetupFlow(userID, text, lang); handled {
		return resp, nil
	}
	if text == "/help" || text == "/start" {
		return a.msg(lang, "help"), nil
	}
	if text == "/status" {
		return a.handleStatus(lang), nil
	}
	if text == "/clear" {
		a.history.Clear(userID)
		if lang == "zh" { return "🧹 对话记忆已清除。", nil }
		return "🧹 Conversation history cleared.", nil
	}
	if resp, handled := a.handleTradeConfirmation(ctx, userID, text, lang); handled {
		return resp, nil
	}
	if trade := parseTradeCommand(text); trade != nil {
		a.pending.Add(trade)
		a.pending.CleanExpired()
		return formatTradeConfirmation(trade, lang), nil
	}

	return a.thinkAndActStream(ctx, userID, lang, text, onEvent)
}

// thinkAndAct sends the user message to LLM with full context and tools.
// The LLM decides what to do — analyze, query, trade, or just chat.
// Supports a tool-calling loop: LLM can call tools, get results, and continue.
func (a *Agent) thinkAndAct(ctx context.Context, userID int64, lang, text string) (string, error) {
	if a.aiClient == nil {
		return a.noAIFallback(lang, text)
	}

	// Build rich context for the LLM
	systemPrompt := a.buildSystemPrompt(lang)

	// Enrich with real-time data if any asset is mentioned
	enrichment := a.gatherContext(text)

	userPrompt := text
	if enrichment != "" {
		userPrompt = text + "\n\n---\n[NOFXi System Context - real-time data for reference]\n" + enrichment
	}

	// Build messages with conversation history
	messages := []mcp.Message{mcp.NewSystemMessage(systemPrompt)}

	// Add conversation history (up to last N messages)
	history := a.history.Get(userID)
	for _, msg := range history {
		messages = append(messages, mcp.NewMessage(msg.Role, msg.Content))
	}

	// Add current user message
	messages = append(messages, mcp.NewUserMessage(userPrompt))

	// Record user message in history
	a.history.Add(userID, "user", text)

	// Define tools for the LLM
	tools := agentTools()

	// Tool-calling loop (max 5 iterations to prevent infinite loops)
	const maxToolRounds = 5
	for round := 0; round < maxToolRounds; round++ {
		req := &mcp.Request{
			Messages:   messages,
			Tools:      tools,
			ToolChoice: "auto",
			Ctx:        ctx,
		}

		resp, err := a.aiClient.CallWithRequestFull(req)
		if err != nil {
			a.logger.Error("LLM call failed", "error", err, "round", round)
			if round == 0 {
				// First round failed — try without tools as fallback
				plainReq := &mcp.Request{Messages: messages, Ctx: ctx}
				plainResp, plainErr := a.aiClient.CallWithRequest(plainReq)
				if plainErr != nil {
					return a.noAIFallback(lang, text)
				}
				a.history.Add(userID, "assistant", plainResp)
				return plainResp, nil
			}
			return a.noAIFallback(lang, text)
		}

		// If LLM returned a text response (no tool calls), we're done
		if len(resp.ToolCalls) == 0 {
			a.history.Add(userID, "assistant", resp.Content)
			return resp.Content, nil
		}

		// LLM wants to call tools — process each one
		a.logger.Info("LLM tool calls", "count", len(resp.ToolCalls), "round", round)

		// Add assistant message with tool calls to conversation
		assistantMsg := mcp.Message{
			Role:      "assistant",
			ToolCalls: resp.ToolCalls,
		}
		if resp.Content != "" {
			assistantMsg.Content = resp.Content
		}
		messages = append(messages, assistantMsg)

		// Execute each tool call and add results
		for _, tc := range resp.ToolCalls {
			a.logger.Info("executing tool",
				"name", tc.Function.Name,
				"args", tc.Function.Arguments,
				"call_id", tc.ID,
			)

			result := a.handleToolCall(ctx, userID, lang, tc)

			// Add tool result message
			messages = append(messages, mcp.Message{
				Role:       "tool",
				Content:    result,
				ToolCallID: tc.ID,
			})
		}

		// Continue the loop — LLM will see tool results and either respond or call more tools
	}

	// If we exhausted all rounds, ask LLM for a final text response without tools
	finalReq := &mcp.Request{Messages: messages, Ctx: ctx}
	finalResp, err := a.aiClient.CallWithRequest(finalReq)
	if err != nil {
		return a.noAIFallback(lang, text)
	}
	a.history.Add(userID, "assistant", finalResp)
	return finalResp, nil
}

// StreamEvent types sent via SSE to the frontend.
const (
	StreamEventTool  = "tool"  // Tool is being called (shows status to user)
	StreamEventDelta = "delta" // Text chunk from LLM streaming
	StreamEventDone  = "done"  // Stream complete
	StreamEventError = "error" // Error occurred
)

// thinkAndActStream is like thinkAndAct but streams the final LLM response via SSE.
// Tool-calling rounds use non-streaming CallWithRequestFull.
// Once tools are done, the final response is streamed via onEvent("delta", accumulated_text).
// onEvent("tool", toolName) is sent when a tool is being called.
func (a *Agent) thinkAndActStream(ctx context.Context, userID int64, lang, text string, onEvent func(event, data string)) (string, error) {
	if a.aiClient == nil {
		return a.noAIFallback(lang, text)
	}

	systemPrompt := a.buildSystemPrompt(lang)
	enrichment := a.gatherContext(text)
	userPrompt := text
	if enrichment != "" {
		userPrompt = text + "\n\n---\n[NOFXi System Context - real-time data for reference]\n" + enrichment
	}

	messages := []mcp.Message{mcp.NewSystemMessage(systemPrompt)}
	for _, msg := range a.history.Get(userID) {
		messages = append(messages, mcp.NewMessage(msg.Role, msg.Content))
	}
	messages = append(messages, mcp.NewUserMessage(userPrompt))
	a.history.Add(userID, "user", text)

	tools := agentTools()

	// Tool-calling loop with streaming:
	// 1. Non-streaming call with tools to detect if LLM needs tools
	// 2. If tools needed: execute them, loop back
	// 3. When done (no more tools): stream the final response via SSE
	const maxToolRounds = 5
	toolsUsed := false

	for round := 0; round < maxToolRounds; round++ {
		req := &mcp.Request{Messages: messages, Tools: tools, ToolChoice: "auto", Ctx: ctx}
		resp, err := a.aiClient.CallWithRequestFull(req)
		if err != nil {
			a.logger.Error("LLM call failed (stream)", "error", err, "round", round)
			if round == 0 {
				// First round failed — try streaming without tools as fallback
				streamReq := &mcp.Request{Messages: messages, Ctx: ctx}
				streamText, streamErr := a.aiClient.CallWithRequestStream(streamReq, func(chunk string) {
					onEvent(StreamEventDelta, chunk)
				})
				if streamErr != nil {
					return a.noAIFallback(lang, text)
				}
				a.history.Add(userID, "assistant", streamText)
				return streamText, nil
			}
			return a.noAIFallback(lang, text)
		}

		// No tool calls → done with tool loop
		if len(resp.ToolCalls) == 0 {
			if !toolsUsed {
				// No tools were ever called — the non-streaming probe already has the answer.
				// Emit as a single delta so frontend renders it immediately.
				onEvent(StreamEventDelta, resp.Content)
				a.history.Add(userID, "assistant", resp.Content)
				return resp.Content, nil
			}
			// Tools were used in previous rounds, LLM gave final answer without streaming.
			// This shouldn't normally happen (we break and stream below), but handle it.
			onEvent(StreamEventDelta, resp.Content)
			a.history.Add(userID, "assistant", resp.Content)
			return resp.Content, nil
		}

		// Process tool calls
		toolsUsed = true
		a.logger.Info("LLM tool calls (stream)", "count", len(resp.ToolCalls), "round", round)
		assistantMsg := mcp.Message{Role: "assistant", ToolCalls: resp.ToolCalls}
		if resp.Content != "" {
			assistantMsg.Content = resp.Content
		}
		messages = append(messages, assistantMsg)

		for _, tc := range resp.ToolCalls {
			onEvent(StreamEventTool, tc.Function.Name)
			a.logger.Info("executing tool", "name", tc.Function.Name, "call_id", tc.ID)
			result := a.handleToolCall(ctx, userID, lang, tc)
			messages = append(messages, mcp.Message{Role: "tool", Content: result, ToolCallID: tc.ID})
		}

		// After tool execution, stream the next LLM response for real-time UX.
		// Omit tools so LLM can't start another tool round — it must produce text.
		streamReq := &mcp.Request{Messages: messages, Ctx: ctx}
		streamText, streamErr := a.aiClient.CallWithRequestStream(streamReq, func(chunk string) {
			onEvent(StreamEventDelta, chunk)
		})
		if streamErr != nil {
			a.logger.Error("stream post-tool response failed", "error", streamErr, "round", round)
			return a.noAIFallback(lang, text)
		}
		a.history.Add(userID, "assistant", streamText)
		return streamText, nil
	}

	// Exhausted all tool rounds — stream the final synthesis response
	finalReq := &mcp.Request{Messages: messages, Ctx: ctx}
	finalText, err := a.aiClient.CallWithRequestStream(finalReq, func(chunk string) {
		onEvent(StreamEventDelta, chunk)
	})
	if err != nil {
		a.logger.Error("stream final response failed", "error", err)
		return a.noAIFallback(lang, text)
	}
	a.history.Add(userID, "assistant", finalText)
	return finalText, nil
}

// buildSystemPrompt creates the system prompt that makes NOFXi behave like a real agent.
func (a *Agent) buildSystemPrompt(lang string) string {
	// Gather live system state
	traderInfo := a.getTradersSummary()
	watchlist := ""
	if a.sentinel != nil {
		watchlist = a.sentinel.FormatWatchlist(lang)
	}

	if lang == "zh" {
		return fmt.Sprintf(`你是 NOFXi，一个专业的 AI 交易 Agent。你不是一个简单的聊天机器人——你是用户的交易伙伴。

## 你的核心能力
1. **市场分析** — 加密货币（BTC/ETH/SOL等）有实时数据，A股/港股/美股/外汇你可以基于知识分析
2. **交易管理** — 查看持仓、余额、交易历史、Trader 状态
3. **策略建议** — 根据用户需求制定交易策略
4. **风险管理** — 评估风险、建议止损止盈
5. **配置引导** — 用户说"开始配置"时引导配置交易所和AI模型

## 当前系统状态
%s
%s

## 数据说明（极其重要，违反即失职！）
- 加密货币（BTC/ETH等）：交易所实时数据，标注 [Real-time]
- A股/港股/美股：**必须调用 search_stock 工具**获取实时行情。不调工具就没有数据。
- 美股盘前盘后：search_stock 返回的 quote 中 ext_price/ext_change_pct/ext_time
- 外汇/指数期货：当前没有数据源，如实告知

### 铁律：禁止编造任何价格！
- **你的训练数据中的价格全部过时，不可使用**
- **没有通过工具获取的价格 = 你不知道 = 不能说**
- 用户问多只股票的盘前数据？→ 对每只股票调用 search_stock 工具
- 用户问"盘前概览"？→ 调用 search_stock 查主要股票（AAPL、TSLA、NVDA、MSFT、GOOGL、AMZN、META等），用真实数据回答
- **绝对不允许**不调工具就给出具体价格数字（如 $421.85）
- 如果某只股票 search_stock 查不到数据，就说"暂时无法获取该股票数据"
- 指数期货（纳指、标普、道琼斯期货）我们目前没有数据源，直接说"暂不支持指数期货数据"

## 工具使用
你可以调用以下工具来执行操作：
- **search_stock** — 搜索股票（支持中文名、英文名、代码）。当用户提到你不认识的股票时，先用这个工具搜索。
- **execute_trade** — 下单交易（加密货币或美股）。美股：open_long=买入，close_long=卖出。调用后创建待确认订单，用户需回复"确认 trade_xxx"。
- **get_positions** — 查看当前所有持仓（加密货币 + 股票）
- **get_balance** — 查看账户余额
- **get_market_price** — 获取实时价格（加密货币或股票代码）

### 交易安全规则
- 用户明确要求交易时才调用 execute_trade
- 分析和建议不需要调用工具，直接回复即可
- 交易确认信息要清晰展示：品种、方向、数量、杠杆
- 提醒用户确认命令格式

### 数据真实性规则（极其重要！）
- **持仓信息必须且只能通过 get_positions 工具获取**，绝对禁止编造持仓
- **余额信息必须且只能通过 get_balance 工具获取**，绝对禁止编造余额
- 如果用户问持仓但 get_positions 返回空，就说"当前没有持仓"，不要编造
- 如果工具返回 error（如未配置交易所），如实告知用户
- **你不知道用户持有什么股票/币种，除非工具返回了数据**
- 查股票行情 ≠ 用户持有该股票。不要混淆"查价格"和"有持仓"

## 行为准则
- 简洁、专业、有观点。不说废话。
- 用户问什么答什么，不要推销配置。
- 有实时数据时给具体价位，没有时给策略框架和思路。
- **诚实是第一原则** — 不确定就说不确定，没数据就说没数据。绝不编造。
- 用交易相关的 emoji 让回复更直观。
- 用中文回复。

当前时间: %s`, traderInfo, watchlist, time.Now().Format("2006-01-02 15:04:05"))
	}

	return fmt.Sprintf(`You are NOFXi, a professional AI trading agent. Not a chatbot — a trading partner.

## Capabilities
1. Market analysis — crypto with real-time data, stocks/forex with knowledge
2. Trade management — positions, balance, history, trader status
3. Strategy — build trading strategies based on user needs
4. Risk management — assess risk, suggest stop-loss/take-profit
5. Setup — guide exchange/AI configuration when user asks

## Current System State
%s
%s

## Data Notice (CRITICAL — violating this is unacceptable!)
- Crypto (BTC/ETH): Exchange real-time data, marked [Real-time]
- Stocks: You MUST call search_stock tool to get real-time quotes. No tool call = no data.
- US stocks pre/after-hours: ext_price/ext_change_pct/ext_time in search_stock results
- Forex/Index futures: No data source currently — tell user honestly

### ABSOLUTE RULE: NEVER fabricate any price!
- Your training data prices are ALL outdated and MUST NOT be used
- No tool result = you don't know = you cannot state a price
- User asks multiple stocks? → Call search_stock for EACH one
- User asks "pre-market overview"? → Call search_stock for major stocks (AAPL, TSLA, NVDA, MSFT, GOOGL, AMZN, META etc.) and use real data
- NEVER output a specific price number (like $421.85) without a tool having returned it
- If search_stock fails for a stock, say "unable to fetch data for this stock"
- Index futures (NDX, SPX, DJI futures) — we have no data source, say "index futures not supported yet"

## Tools
You can call these tools to take action:
- **search_stock** — Search for stocks by name, ticker, or code. Covers A-share, HK, and US markets. Use when the user mentions an unknown stock.
- **execute_trade** — Place a trade order (crypto or US stocks). For stocks: open_long=buy, close_long=sell. Creates a pending order that requires user confirmation.
- **get_positions** — View all current open positions (crypto + stocks)
- **get_balance** — View account balance and equity
- **get_market_price** — Get real-time price from the exchange (crypto or stock symbol)

### Trade Safety Rules
- Only call execute_trade when user explicitly requests a trade
- Analysis and advice don't need tools — just reply directly
- Show trade details clearly: symbol, direction, quantity, leverage
- Remind user of the confirmation command format

### Data Truthfulness Rules (CRITICAL!)
- **Position data MUST come from get_positions tool only** — NEVER fabricate positions
- **Balance data MUST come from get_balance tool only** — NEVER fabricate balances
- If get_positions returns empty, say "no open positions" — do NOT make up holdings
- If a tool returns an error (e.g. no exchange configured), tell the user honestly
- **You do NOT know what the user holds unless a tool tells you**
- Checking a stock price ≠ user owns that stock. Never confuse "quote lookup" with "holding"

## Behavior
- Concise, professional, opinionated. No fluff.
- Answer what's asked. Don't push setup.
- With real-time data: give specific levels. Without: give strategy frameworks.
- **Honesty is rule #1** — uncertain = say uncertain, no data = say no data.
- Use trading emojis.

Current time: %s`, traderInfo, watchlist, time.Now().Format("2006-01-02 15:04:05"))
}

// gatherContext collects real-time market data relevant to the user's message.
func (a *Agent) gatherContext(text string) string {
	var parts []string
	upper := strings.ToUpper(text)

	// Crypto — detect symbols dynamically
	// 1. Check known popular symbols (fast path)
	// 2. Extract any "XXXUSDT" pattern from text (catches arbitrary pairs)
	knownSymbols := []string{
		"BTC", "ETH", "SOL", "BNB", "XRP", "DOGE", "ADA", "AVAX", "DOT", "LINK",
		"PEPE", "SHIB", "ARB", "OP", "SUI", "APT", "SEI", "TIA", "JUP", "WIF",
		"NEAR", "ATOM", "FTM", "MATIC", "INJ", "RENDER", "FET", "TAO", "WLD",
		"AAVE", "UNI", "LDO", "MKR", "CRV", "PENDLE", "ENA", "ONDO", "TRUMP",
	}
	matched := make(map[string]bool)
	for _, sym := range knownSymbols {
		if strings.Contains(upper, sym) {
			matched[sym] = true
		}
	}
	// Also extract "XXXUSDT" patterns for coins not in the known list
	for _, word := range strings.Fields(upper) {
		word = strings.Trim(word, ".,!?;:()[]{}\"'")
		if strings.HasSuffix(word, "USDT") && len(word) > 4 && len(word) <= 15 {
			sym := strings.TrimSuffix(word, "USDT")
			if len(sym) >= 2 && len(sym) <= 10 {
				matched[sym] = true
			}
		}
	}
	// Collect and sort matched symbols for deterministic selection
	sortedSymbols := make([]string, 0, len(matched))
	for sym := range matched {
		sortedSymbols = append(sortedSymbols, sym)
	}
	sort.Strings(sortedSymbols)

	// Cap at 5 symbols to avoid slow context gathering
	count := 0
	for _, sym := range sortedSymbols {
		if count >= 5 { break }
		md, err := market.Get(sym + "USDT")
		if err == nil && md.CurrentPrice > 0 {
			parts = append(parts, fmt.Sprintf("[%s/USDT Real-time]\nPrice: $%.4f | 1h: %+.2f%% | 4h: %+.2f%% | RSI7: %.1f | EMA20: %.4f | MACD: %.6f | Funding: %.4f%%",
				sym, md.CurrentPrice, md.PriceChange1h, md.PriceChange4h, md.CurrentRSI7, md.CurrentEMA20, md.CurrentMACD, md.FundingRate*100))
			count++
		}
	}

	// A-share / stocks — try Sina Finance (dynamic search as fallback)
	stockCode, stockName := resolveStockCodeDynamic(text)
	if stockCode != "" {
		quote, err := fetchStockQuote(stockCode)
		if err == nil && quote.Price > 0 {
			parts = append(parts, fmt.Sprintf("[%s(%s) Real-time A-share Data]\n%s", quote.Name, quote.Code, formatStockQuote(quote)))
		} else if err != nil {
			a.logger.Error("fetch stock quote", "code", stockCode, "name", stockName, "error", err)
		}
	}

	// Trader positions
	if a.traderManager != nil {
		for _, t := range a.traderManager.GetAllTraders() {
			positions, err := t.GetPositions()
			if err != nil { continue }
			for _, p := range positions {
				size := toFloat(p["size"])
				if size == 0 { continue }
				parts = append(parts, fmt.Sprintf("[Position] %s %s: size=%.4f entry=$%.4f mark=$%.4f pnl=$%.2f",
					p["symbol"], p["side"], size, toFloat(p["entryPrice"]), toFloat(p["markPrice"]), toFloat(p["unrealizedPnl"])))
			}
		}
	}

	return strings.Join(parts, "\n")
}

func (a *Agent) getTradersSummary() string {
	if a.traderManager == nil { return "Traders: none configured" }
	traders := a.traderManager.GetAllTraders()
	if len(traders) == 0 { return "Traders: none configured" }

	var lines []string
	for id, t := range traders {
		s := t.GetStatus()
		running, _ := s["is_running"].(bool)
		status := "stopped"
		if running { status = "running" }
		tid := id
		if len(tid) > 8 { tid = tid[:8] }
		lines = append(lines, fmt.Sprintf("• %s [%s] %s | %s", t.GetName(), tid, status, t.GetExchange()))
	}
	return "Traders:\n" + strings.Join(lines, "\n")
}

func (a *Agent) handleStatus(L string) string {
	tc, rc := 0, 0
	if a.traderManager != nil {
		all := a.traderManager.GetAllTraders()
		tc = len(all)
		for _, t := range all {
			if s := t.GetStatus(); s["is_running"] == true { rc++ }
		}
	}
	wc := 0
	if a.sentinel != nil { wc = a.sentinel.SymbolCount() }
	ai := "❌"
	if a.aiClient != nil { ai = "✅" }
	return fmt.Sprintf(a.msg(L, "status"), rc, tc, wc, ai, time.Now().Format("2006-01-02 15:04:05"))
}

// noAIFallback — when no AI is available, still try to be useful.
func (a *Agent) noAIFallback(lang, text string) (string, error) {
	upper := strings.ToUpper(text)

	// Try to provide market data directly
	for _, sym := range []string{"BTC", "ETH", "SOL", "BNB", "XRP", "DOGE"} {
		if strings.Contains(upper, sym) {
			md, err := market.Get(sym + "USDT")
			if err == nil {
				return fmt.Sprintf("📊 *%s/USDT*\n\n%s\n\n💡 配置 AI 模型后我能给你更深度的分析。发送 *开始配置* 开始。", sym, market.Format(md)), nil
			}
		}
	}

	// Check if asking about positions/balance
	if strings.Contains(text, "持仓") || strings.Contains(upper, "POSITION") {
		return a.queryPositionsDirect(lang)
	}
	if strings.Contains(text, "余额") || strings.Contains(upper, "BALANCE") {
		return a.queryBalancesDirect(lang)
	}

	if lang == "zh" {
		return "🤖 我是 NOFXi。配置 AI 模型后我就能理解你的任何问题——分析股票、制定策略、管理交易。\n\n现在可用：\n• 加密货币实时行情（试试「BTC」）\n• `/status` 系统状态\n\n发送 *开始配置* 配置 AI 模型。", nil
	}
	return "🤖 I'm NOFXi. Configure an AI model and I can understand anything — analyze stocks, build strategies, manage trades.\n\nAvailable now:\n• Crypto real-time data (try 'BTC')\n• `/status` system status\n\nSend *setup* to configure AI.", nil
}

func (a *Agent) queryPositionsDirect(L string) (string, error) {
	if a.traderManager == nil { return a.msg(L, "no_traders"), nil }
	var sb strings.Builder
	sb.WriteString("📊 *Positions*\n\n")
	hasAny := false
	for id, t := range a.traderManager.GetAllTraders() {
		positions, err := t.GetPositions()
		if err != nil { continue }
		for _, p := range positions {
			size := toFloat(p["size"])
			if size == 0 { continue }
			hasAny = true
			pnl := toFloat(p["unrealizedPnl"])
			e := "🟢"; if pnl < 0 { e = "🔴" }
			sb.WriteString(fmt.Sprintf("%s *%s* %s — $%.2f | Trader: %s\n", e, p["symbol"], p["side"], pnl, id[:8]))
		}
	}
	if !hasAny { return a.msg(L, "no_positions"), nil }
	return sb.String(), nil
}

func (a *Agent) queryBalancesDirect(L string) (string, error) {
	if a.traderManager == nil { return a.msg(L, "no_traders"), nil }
	var sb strings.Builder
	sb.WriteString("💰 *Balance*\n\n")
	for id, t := range a.traderManager.GetAllTraders() {
		info, err := t.GetAccountInfo()
		if err != nil { continue }
		tid := id; if len(tid) > 8 { tid = tid[:8] }
		sb.WriteString(fmt.Sprintf("*%s* (%s): $%.2f\n", t.GetName(), tid, toFloat(info["total_equity"])))
	}
	return sb.String(), nil
}

func (a *Agent) handleSignal(sig Signal) {
	if a.brain != nil { a.brain.HandleSignal(sig) }
}

func (a *Agent) notifyAll(text string) {
	if a.NotifyFunc != nil { a.NotifyFunc(0, text) }
}

func toFloat(v interface{}) float64 {
	switch x := v.(type) {
	case float64: return x
	case float32: return float64(x)
	case int: return float64(x)
	case int64: return float64(x)
	case int32: return float64(x)
	case string: f, _ := strconv.ParseFloat(x, 64); return f
	case json.Number: f, _ := x.Float64(); return f
	}
	return 0
}