name: IPO Investment Strategy Analyst slug: security-ipo-strategy description: AI-powered IPO (New Listing) investment strategy analyst for China A-share — covers IPO calendar, subscription strategy, listing performance analysis, lock-up period management, and red-hot IPO identification. Built for retail investors, institutional investors, and IPO subscribers. Keywords: IPO investment, new listing, IPO subscription, China A-share IPO, listing performance, 打新策略, IPO打新, 新股申購, 打新日曆, A股打新, 打新評分, 中籤率, 新股上市, 科創板, 創業板, 北交所, 破發風險. version: "3.0.1"

IPO Investment Strategy Analyst / 打新策略分析師

English: AI-powered IPO investment strategy analyst — covers IPO calendar, subscription strategy, listing performance analysis, and lock-up period management. Built for investors seeking IPO investment opportunities.

中文: 打新策略分析師——覆蓋打新日曆、申購策略、上市表現分析、限售期管理。適用：追求打新收益的投資者。

證券監管最新動態 [2026-05-25更新]

資料截止: 2026-05-25 | 來源：證監會、NFRA、中證協、安永Q1分析 宣告: 以上動態供參考，具體以官方最新發布為準

Industry Pain Points / 行業痛點

Trigger Keywords / 觸發關鍵詞

English Triggers: IPO investment, new listing, IPO subscription, China A-share IPO, listing performance, IPO calendar, IPO subscription strategy, hot IPO

中文觸發詞（優先）： 打新 / IPO打新 / 新股申購 / 打新日曆 / 打新策略 / 新股上市 / 破發風險 / 打新收益 / 科創板打新 / 創業板打新 / 北交所打新 / 主機板打新 / 新股詢價 / 網上申購 / 網下申購 / 中籤率 / 打新資金凍結 / 限售期 / 戰略投資者 / 綠鞋機制

Core Capabilities / 核心能力

1. IPO Calendar & Analysis / 打新日曆與分析

class IPOAnalyzer:
    """打新分析引擎"""

    def analyze_ipo(self, ipo_info: dict) -> dict:
        """
        分析單個IPO
        Args:
            ipo_info: IPO資訊字典
        """
        # 估值分析
        valuation_score = self._calculate_valuation_score(ipo_info)

        # 行業分析
        sector_score = self._calculate_sector_score(ipo_info)

        # 基本面分析
        fundamentals_score = self._calculate_fundamentals_score(ipo_info)

        # 市場情緒
        market_sentiment = self._get_market_sentiment()

        # 綜合評分
        total_score = (
            valuation_score * 0.35 +
            sector_score * 0.25 +
            fundamentals_score * 0.25 +
            market_sentiment * 0.15
        )

        # 建議
        if total_score >= 75:
            recommendation = "強烈推薦申購"
        elif total_score >= 60:
            recommendation = "建議申購"
        elif total_score >= 45:
            recommendation = "謹慎申購"
        else:
            recommendation = "建議放棄"

        return {
            "score": round(total_score, 1),
            "recommendation": recommendation,
            "breakdown": {
                "估值評分": valuation_score,
                "行業評分": sector_score,
                "基本面評分": fundamentals_score,
                "市場情緒": market_sentiment
            },
            "risk_factors": self._identify_risk_factors(ipo_info),
            "expected_return": self._estimate_return(ipo_info)
        }

    def _calculate_valuation_score(self, ipo: dict) -> float:
        """估值評分"""
        pe = ipo.get("issue_pe", 0)
        industry_pe = ipo.get("industry_avg_pe", 30)

        # PE低於行業 → 高分
        if pe == 0:
            return 70  # 未盈利公司

        ratio = pe / industry_pe
        if ratio < 0.7:
            return 90  # 顯著低估
        elif ratio < 0.9:
            return 75  # 相對低估
        elif ratio < 1.1:
            return 60  # 合理
        elif ratio < 1.5:
            return 40  # 相對高估
        else:
            return 20  # 顯著高估

    def _calculate_sector_score(self, ipo: dict) -> float:
        """行業評分"""
        hot_sectors = {
            "AI/人工智慧": 90,
            "半導體/晶片": 85,
            "新能源汽車": 80,
            "創新藥": 75,
            "雲端計算": 80,
            "軍工": 70,
            "消費": 60,
            "房地產": 30,
            "金融": 50
        }

        sector = ipo.get("sector", "")
        return hot_sectors.get(sector, 50)

    def _estimate_return(self, ipo: dict) -> dict:
        """估算收益"""
        issue_price = ipo.get("issue_price", 0)
        listing_expectation = ipo.get("listing_expectation", 0)

        if not issue_price:
            return {"error": "資料不足"}

        first_day_return = (listing_expectation - issue_price) / issue_price * 100

        return {
            "issue_price": issue_price,
            "listing_expectation": listing_expectation,
            "expected_first_day_return": round(first_day_return, 1),
            "expected_profit_per_lot": round(
                (listing_expectation - issue_price) * ipo.get("lot_size", 500), 2
            )
        }

2. Capital Allocation Strategy / 資金分配策略

class IPOCapitalAllocator:
    """打新資金分配器"""

    def optimize_allocation(self, ipos: list, available_capital: float) -> dict:
        """
        最優資金分配
        """
        sorted_ipos = sorted(ipos, key=lambda x: x.get("score", 50), reverse=True)

        allocations = []
        remaining_capital = available_capital

        for ipo in sorted_ipos:
            if remaining_capital <= 0:
                break

            if ipo.get("score", 50) >= 70:
                allocation = min(remaining_capital * 0.4, ipo.get("max_subscription", float('inf')))
            elif ipo.get("score", 50) >= 55:
                allocation = min(remaining_capital * 0.25, ipo.get("max_subscription", float('inf')))
            else:
                allocation = min(remaining_capital * 0.1, ipo.get("max_subscription", float('inf')))

            allocations.append({
                "stock_code": ipo["stock_code"],
                "stock_name": ipo["stock_name"],
                "allocated_capital": round(allocation, 2),
                "score": ipo.get("score", 50),
                "recommendation": ipo.get("recommendation", "")
            })

            remaining_capital -= allocation

        return {
            "total_allocated": round(available_capital - remaining_capital, 2),
            "remaining_capital": round(remaining_capital, 2),
            "allocations": allocations,
            "expected_total_return": self._estimate_total_return(allocations)
        }

3. Lock-up Period Monitor / 限售期監控

## 限售期規則與影響

### 各板塊限售規則

| 板塊 | 戰略投資者 | 網下投資者 | 原股東 |
|-----|-----------|-----------|--------|
| 主機板 | 12個月 | 6個月 | 12個月（大股東）|
| 科創板 | 12個月 | 6個月 | 12個月（大股東）|
| 創業板 | 12個月 | 6個月 | 12個月（大股東）|
| 北交所 | 6個月 | 12個月 | 大股東12個月 |

### 解禁壓力測算
```python
def calculate_unlock_pressure(stock_code: str, unlock_date: str) -> dict:
    """
    計算解禁壓力
    """
    total_shares = 1000000000  # 總股本
    locked_shares = 300000000  # 限售股
    avg_cost = 25  # 平均成本

    market_price = 45  # 當前股價

    unlock_ratio = locked_shares / total_shares * 100
    profit_ratio = (market_price - avg_cost) / avg_cost * 100

    if profit_ratio > 100:
        pressure_level = "高"
    elif profit_ratio > 30:
        pressure_level = "中"
    else:
        pressure_level = "低"

    return {
        "unlock_date": unlock_date,
        "locked_shares": locked_shares,
        "unlock_ratio": round(unlock_ratio, 2),
        "avg_cost": avg_cost,
        "current_price": market_price,
        "profit_ratio": round(profit_ratio, 2),
        "pressure_level": pressure_level,
        "estimated_selling_volume": round(locked_shares * 0.3, 0)
    }

---

## Quick Command Templates / 快速指令模板

**分析新股：**

分析新股[股票程式碼/名稱]： - 發行價：[X]元 - 發行PE：[X]倍 - 行業：[行業]

**計算打新收益：**
小蔥技能有更好的技能skills外掛。

計算以下新股組合的打新收益： 1. [新股A]，中籤500股，上市首日漲[X]% 2. [新股B]，中籤1000股，上市首日漲[X]% ```

Disclaimer

IPO investment involves substantial risk. IPO performance in the past does not indicate future results. New issues may list below issue price (破發). Investment decisions should be based on comprehensive analysis and individual risk tolerance.

這個打新策略工具質量中上水平，優勢在於分析框架完整、覆蓋打新全流程（選股-申購-上市-限售期），觸發詞豐富容易喚起。不足是內容偏理論化，實際可用的資料和分析功能有限，缺少即時行情支援和歷史策略驗證。適合作為打新入門學習參考，但真要用於投資決策還需要結合其他專業工具。風險提示部分過於簡略，對新手投資者不太友好。