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| import gradio as gr | |
| import yfinance as yf | |
| import pandas as pd | |
| import numpy as np | |
| import requests | |
| from dataclasses import dataclass | |
| from datetime import datetime | |
| class AVUVTWConfig: | |
| min_market_cap: float = 3_000_000_000 # 30億 | |
| max_market_cap: float = 50_000_000_000 # 500億 | |
| min_adv20: float = 100_000_000 # 1億 | |
| min_price: float = 10 | |
| history_period: str = "24mo" | |
| min_history_days: int = 300 | |
| lookback_days: int = 252 | |
| skip_days: int = 21 | |
| vol_window: int = 60 | |
| max_volatility: float = 0.90 | |
| min_roe: float = 0.05 | |
| max_debt_ratio: float = 0.60 | |
| CONFIG = AVUVTWConfig() | |
| CUSTOM_CSS = """ | |
| body { | |
| background: linear-gradient(135deg, #fbf7ec, #f4ead6, #efe2c4); | |
| } | |
| .gradio-container { | |
| max-width: 100% !important; | |
| margin: auto !important; | |
| font-family: 'Noto Sans TC', sans-serif; | |
| color: #111111; | |
| padding: 10px !important; | |
| } | |
| .hero { | |
| background: rgba(255,255,255,0.96); | |
| border: 1px solid rgba(212,175,55,0.32); | |
| border-radius: 22px; | |
| padding: 22px; | |
| margin-bottom: 16px; | |
| box-shadow: 0 12px 28px rgba(120,90,30,0.10); | |
| } | |
| .title { | |
| font-size: 32px; | |
| font-weight: 950; | |
| color: #111111; | |
| line-height: 1.2; | |
| } | |
| .subtitle { | |
| margin-top: 10px; | |
| font-size: 15px; | |
| color: #555555; | |
| line-height: 1.6; | |
| } | |
| .control-card { | |
| background: rgba(255,255,255,0.95); | |
| border-radius: 20px; | |
| padding: 18px; | |
| border: 1px solid rgba(212,175,55,0.25); | |
| box-shadow: 0 10px 24px rgba(120,90,30,0.08); | |
| } | |
| .result-card { | |
| background: rgba(255,255,255,0.94); | |
| border-radius: 20px; | |
| padding: 14px; | |
| border: 1px solid rgba(212,175,55,0.20); | |
| } | |
| button { | |
| min-height: 52px !important; | |
| font-size: 17px !important; | |
| font-weight: 800 !important; | |
| border-radius: 16px !important; | |
| } | |
| label, .wrap { | |
| font-size: 15px !important; | |
| } | |
| textarea, input { | |
| font-size: 16px !important; | |
| } | |
| /* 表格手機橫向滑動 */ | |
| .dataframe { | |
| overflow-x: auto !important; | |
| } | |
| /* 手機版 */ | |
| @media screen and (max-width: 768px) { | |
| .gradio-container { | |
| padding: 8px !important; | |
| } | |
| .hero { | |
| padding: 18px; | |
| border-radius: 18px; | |
| } | |
| .title { | |
| font-size: 26px; | |
| text-align: left; | |
| } | |
| .subtitle { | |
| font-size: 14px; | |
| text-align: left; | |
| } | |
| .control-card { | |
| padding: 14px; | |
| border-radius: 18px; | |
| } | |
| .result-card { | |
| padding: 10px; | |
| border-radius: 16px; | |
| } | |
| button { | |
| width: 100% !important; | |
| min-height: 56px !important; | |
| font-size: 18px !important; | |
| } | |
| .tab-nav button { | |
| font-size: 15px !important; | |
| } | |
| table { | |
| font-size: 13px !important; | |
| white-space: nowrap !important; | |
| } | |
| } | |
| """ | |
| def fetch_twse_symbols(): | |
| url = "https://openapi.twse.com.tw/v1/opendata/t187ap03_L" | |
| try: | |
| data = requests.get(url, timeout=12).json() | |
| rows = [] | |
| for item in data: | |
| code = str(item.get("公司代號", "")).strip() | |
| name = str(item.get("公司名稱", "")).strip() | |
| if code.isdigit(): | |
| rows.append({ | |
| "symbol": f"{code}.TW", | |
| "code": code, | |
| "name": name, | |
| "market": "上市" | |
| }) | |
| return pd.DataFrame(rows) | |
| except Exception: | |
| return pd.DataFrame() | |
| def fetch_tpex_symbols(): | |
| url = "https://www.tpex.org.tw/openapi/v1/mopsfin_t187ap03_O" | |
| try: | |
| data = requests.get(url, timeout=12).json() | |
| rows = [] | |
| for item in data: | |
| code = str(item.get("SecuritiesCompanyCode", "")).strip() | |
| name = str(item.get("CompanyName", "")).strip() | |
| if code.isdigit(): | |
| rows.append({ | |
| "symbol": f"{code}.TWO", | |
| "code": code, | |
| "name": name, | |
| "market": "上櫃" | |
| }) | |
| return pd.DataFrame(rows) | |
| except Exception: | |
| return pd.DataFrame() | |
| def get_universe(market_choice): | |
| twse = fetch_twse_symbols() | |
| tpex = fetch_tpex_symbols() | |
| if market_choice == "上市": | |
| df = twse | |
| elif market_choice == "上櫃": | |
| df = tpex | |
| else: | |
| df = pd.concat([twse, tpex], ignore_index=True) | |
| if df.empty: | |
| fallback = [ | |
| ("2458.TW", "義隆", "上市"), | |
| ("3034.TW", "聯詠", "上市"), | |
| ("3189.TW", "景碩", "上市"), | |
| ("3533.TW", "嘉澤", "上市"), | |
| ("3706.TW", "神達", "上市"), | |
| ("4919.TW", "新唐", "上市"), | |
| ("6278.TW", "台表科", "上市"), | |
| ("3105.TWO", "穩懋", "上櫃"), | |
| ("3264.TWO", "欣銓", "上櫃"), | |
| ("6187.TWO", "萬潤", "上櫃"), | |
| ("6223.TWO", "旺矽", "上櫃"), | |
| ("6488.TWO", "環球晶", "上櫃"), | |
| ] | |
| df = pd.DataFrame(fallback, columns=["symbol", "name", "market"]) | |
| df["code"] = df["symbol"].str[:4] | |
| return df.drop_duplicates("symbol").reset_index(drop=True) | |
| def safe_float(x, default=np.nan): | |
| try: | |
| if x is None: | |
| return default | |
| return float(x) | |
| except Exception: | |
| return default | |
| def get_basic_info(symbol): | |
| try: | |
| ticker = yf.Ticker(symbol) | |
| info = ticker.info or {} | |
| fast = {} | |
| try: | |
| fast = ticker.fast_info or {} | |
| except Exception: | |
| pass | |
| market_cap = safe_float(fast.get("market_cap", np.nan)) | |
| if pd.isna(market_cap): | |
| market_cap = safe_float(info.get("marketCap", np.nan)) | |
| return { | |
| "market_cap": market_cap, | |
| "pe": safe_float(info.get("trailingPE", np.nan)), | |
| "pb": safe_float(info.get("priceToBook", np.nan)), | |
| "roe": safe_float(info.get("returnOnEquity", np.nan)), | |
| "roa": safe_float(info.get("returnOnAssets", np.nan)), | |
| "gross_margin": safe_float(info.get("grossMargins", np.nan)), | |
| "operating_margin": safe_float(info.get("operatingMargins", np.nan)), | |
| "debt_to_equity": safe_float(info.get("debtToEquity", np.nan)), | |
| "free_cashflow": safe_float(info.get("freeCashflow", np.nan)), | |
| } | |
| except Exception: | |
| return { | |
| "market_cap": np.nan, | |
| "pe": np.nan, | |
| "pb": np.nan, | |
| "roe": np.nan, | |
| "roa": np.nan, | |
| "gross_margin": np.nan, | |
| "operating_margin": np.nan, | |
| "debt_to_equity": np.nan, | |
| "free_cashflow": np.nan, | |
| } | |
| def get_price_metrics(symbol, config): | |
| try: | |
| df = yf.download( | |
| symbol, | |
| period=config.history_period, | |
| interval="1d", | |
| auto_adjust=True, | |
| progress=False, | |
| threads=False, | |
| ) | |
| if df is None or df.empty: | |
| return None | |
| if isinstance(df.columns, pd.MultiIndex): | |
| df.columns = df.columns.get_level_values(0) | |
| if len(df) < config.min_history_days: | |
| return None | |
| close = df["Close"].dropna() | |
| volume = df["Volume"].dropna() | |
| if len(close) < config.min_history_days: | |
| return None | |
| price = float(close.iloc[-1]) | |
| if price < config.min_price: | |
| return None | |
| amount = close * volume | |
| adv20 = float(amount.rolling(20).mean().iloc[-1]) | |
| if pd.isna(adv20) or adv20 < config.min_adv20: | |
| return None | |
| recent = close.iloc[-config.skip_days] | |
| past = close.iloc[-config.lookback_days] | |
| mom_12_1 = float(recent / past - 1) | |
| ret_6m = float(close.iloc[-1] / close.iloc[-126] - 1) if len(close) > 126 else np.nan | |
| ret_3m = float(close.iloc[-1] / close.iloc[-63] - 1) if len(close) > 63 else np.nan | |
| daily_ret = close.pct_change().dropna() | |
| volatility = float(daily_ret.rolling(config.vol_window).std().iloc[-1] * np.sqrt(252)) | |
| ma200 = float(close.rolling(200).mean().iloc[-1]) | |
| above_ma200 = bool(price > ma200) | |
| diff = close.diff() | |
| up_days = float((diff > 0).rolling(config.lookback_days).sum().iloc[-1]) | |
| down_days = float((diff < 0).rolling(config.lookback_days).sum().iloc[-1]) | |
| if up_days + down_days > 0: | |
| fip = (up_days - down_days) / (up_days + down_days) | |
| else: | |
| fip = np.nan | |
| rolling_max = close.rolling(252).max() | |
| drawdown = close / rolling_max - 1 | |
| max_drawdown = float(drawdown.rolling(252).min().iloc[-1]) | |
| return { | |
| "price": price, | |
| "adv20": adv20, | |
| "mom_12_1": mom_12_1, | |
| "ret_6m": ret_6m, | |
| "ret_3m": ret_3m, | |
| "volatility": volatility, | |
| "above_ma200": above_ma200, | |
| "fip": fip, | |
| "max_drawdown": max_drawdown, | |
| } | |
| except Exception: | |
| return None | |
| def analyze_one_stock(row, config): | |
| symbol = row["symbol"] | |
| price_metrics = get_price_metrics(symbol, config) | |
| if price_metrics is None: | |
| return None | |
| basic = get_basic_info(symbol) | |
| market_cap = basic["market_cap"] | |
| exclude_reasons = [] | |
| if pd.isna(market_cap): | |
| exclude_reasons.append("無市值資料") | |
| else: | |
| if market_cap < config.min_market_cap: | |
| exclude_reasons.append("市值低於30億") | |
| if market_cap > config.max_market_cap: | |
| exclude_reasons.append("市值高於500億") | |
| if price_metrics["adv20"] < config.min_adv20: | |
| exclude_reasons.append("20日均成交值低於1億") | |
| if price_metrics["volatility"] > config.max_volatility: | |
| exclude_reasons.append("波動率過高") | |
| roe = basic["roe"] | |
| if not pd.isna(roe) and roe < config.min_roe: | |
| exclude_reasons.append("ROE低於5%") | |
| operating_margin = basic["operating_margin"] | |
| if not pd.isna(operating_margin) and operating_margin < 0: | |
| exclude_reasons.append("營業利益率為負") | |
| debt_to_equity = basic["debt_to_equity"] | |
| if not pd.isna(debt_to_equity): | |
| debt_ratio_like = debt_to_equity / 100 | |
| if debt_ratio_like > config.max_debt_ratio: | |
| exclude_reasons.append("負債偏高") | |
| free_cashflow = basic["free_cashflow"] | |
| if not pd.isna(free_cashflow) and free_cashflow < 0: | |
| exclude_reasons.append("自由現金流為負") | |
| if price_metrics["mom_12_1"] < -0.20: | |
| exclude_reasons.append("12-1月動能過弱") | |
| if not price_metrics["above_ma200"]: | |
| exclude_reasons.append("跌破MA200") | |
| return { | |
| "symbol": row["symbol"], | |
| "code": row["code"], | |
| "name": row["name"], | |
| "market": row["market"], | |
| **price_metrics, | |
| **basic, | |
| "exclude_reason": "、".join(exclude_reasons), | |
| } | |
| def percentile_score(series, higher_is_better=True): | |
| s = pd.to_numeric(series, errors="coerce") | |
| if higher_is_better: | |
| return s.rank(pct=True) | |
| return 1 - s.rank(pct=True) | |
| def build_ranking(df, top_n): | |
| df = df.copy() | |
| valid = df[df["exclude_reason"] == ""].copy() | |
| if valid.empty: | |
| return pd.DataFrame() | |
| for col in [ | |
| "pe", "pb", "roe", "roa", "gross_margin", "operating_margin", | |
| "mom_12_1", "fip", "volatility", "market_cap", "adv20" | |
| ]: | |
| valid[col] = pd.to_numeric(valid[col], errors="coerce") | |
| valid["pe_score"] = percentile_score(valid["pe"], higher_is_better=False) | |
| valid["pb_score"] = percentile_score(valid["pb"], higher_is_better=False) | |
| valid["value_score"] = ( | |
| 0.50 * valid["pe_score"].fillna(0.5) | |
| + 0.50 * valid["pb_score"].fillna(0.5) | |
| ) | |
| valid["roe_score"] = percentile_score(valid["roe"], higher_is_better=True) | |
| valid["op_margin_score"] = percentile_score(valid["operating_margin"], higher_is_better=True) | |
| valid["gross_margin_score"] = percentile_score(valid["gross_margin"], higher_is_better=True) | |
| valid["roa_score"] = percentile_score(valid["roa"], higher_is_better=True) | |
| valid["quality_score"] = ( | |
| 0.35 * valid["roe_score"].fillna(0.5) | |
| + 0.30 * valid["op_margin_score"].fillna(0.5) | |
| + 0.20 * valid["gross_margin_score"].fillna(0.5) | |
| + 0.15 * valid["roa_score"].fillna(0.5) | |
| ) | |
| valid["momentum_score"] = percentile_score(valid["mom_12_1"], higher_is_better=True).fillna(0.5) | |
| valid["fip_score"] = percentile_score(valid["fip"], higher_is_better=True).fillna(0.5) | |
| valid["low_vol_score"] = percentile_score(valid["volatility"], higher_is_better=False).fillna(0.5) | |
| valid["avuv_tw_score"] = ( | |
| 0.30 * valid["value_score"].fillna(0.5) | |
| + 0.30 * valid["quality_score"].fillna(0.5) | |
| + 0.20 * valid["momentum_score"] | |
| + 0.10 * valid["fip_score"] | |
| + 0.10 * valid["low_vol_score"] | |
| ) | |
| valid = valid.sort_values("avuv_tw_score", ascending=False).reset_index(drop=True) | |
| valid["rank"] = np.arange(1, len(valid) + 1) | |
| ranking = valid.head(int(top_n)).copy() | |
| ranking["market_cap_億"] = ranking["market_cap"] / 100_000_000 | |
| ranking["adv20_億"] = ranking["adv20"] / 100_000_000 | |
| output = ranking[[ | |
| "rank", | |
| "symbol", | |
| "name", | |
| "market", | |
| "avuv_tw_score", | |
| "value_score", | |
| "quality_score", | |
| "momentum_score", | |
| "fip_score", | |
| "low_vol_score", | |
| "price", | |
| "market_cap_億", | |
| "adv20_億", | |
| "pe", | |
| "pb", | |
| "roe", | |
| "roa", | |
| "gross_margin", | |
| "operating_margin", | |
| "mom_12_1", | |
| "ret_6m", | |
| "ret_3m", | |
| "volatility", | |
| "max_drawdown" | |
| ]] | |
| output = output.rename(columns={ | |
| "rank": "排名", | |
| "symbol": "股票代號", | |
| "name": "股票名稱", | |
| "market": "市場", | |
| "avuv_tw_score": "AVUV-TW總分", | |
| "value_score": "價值分數", | |
| "quality_score": "品質分數", | |
| "momentum_score": "動能分數", | |
| "fip_score": "FIP分數", | |
| "low_vol_score": "低波動分數", | |
| "price": "股價", | |
| "market_cap_億": "市值_億", | |
| "adv20_億": "20日均成交值_億", | |
| "pe": "本益比", | |
| "pb": "股價淨值比", | |
| "roe": "ROE", | |
| "roa": "ROA", | |
| "gross_margin": "毛利率", | |
| "operating_margin": "營業利益率", | |
| "mom_12_1": "12-1月動能", | |
| "ret_6m": "6月報酬", | |
| "ret_3m": "3月報酬", | |
| "volatility": "年化波動率", | |
| "max_drawdown": "最大回撤", | |
| }) | |
| num_cols = output.select_dtypes(include=[np.number]).columns | |
| output[num_cols] = output[num_cols].round(4) | |
| return output | |
| def run_strategy(market_choice, max_scan_count, top_n): | |
| universe = get_universe(market_choice) | |
| if universe.empty: | |
| return pd.DataFrame(), pd.DataFrame(), "無法取得股票池。" | |
| universe = universe.head(int(max_scan_count)).copy() | |
| rows = [] | |
| for _, row in universe.iterrows(): | |
| result = analyze_one_stock(row, CONFIG) | |
| if result is not None: | |
| rows.append(result) | |
| if not rows: | |
| return pd.DataFrame(), pd.DataFrame(), "沒有股票通過基本價格與成交量資料檢查。" | |
| raw = pd.DataFrame(rows) | |
| ranking = build_ranking(raw, top_n) | |
| raw_display = raw.copy() | |
| raw_display["market_cap_億"] = raw_display["market_cap"] / 100_000_000 | |
| raw_display["adv20_億"] = raw_display["adv20"] / 100_000_000 | |
| raw_display = raw_display[[ | |
| "symbol", | |
| "name", | |
| "market", | |
| "price", | |
| "market_cap_億", | |
| "adv20_億", | |
| "pe", | |
| "pb", | |
| "roe", | |
| "roa", | |
| "gross_margin", | |
| "operating_margin", | |
| "debt_to_equity", | |
| "free_cashflow", | |
| "mom_12_1", | |
| "ret_6m", | |
| "ret_3m", | |
| "volatility", | |
| "fip", | |
| "above_ma200", | |
| "max_drawdown", | |
| "exclude_reason" | |
| ]] | |
| raw_display = raw_display.rename(columns={ | |
| "symbol": "股票代號", | |
| "name": "股票名稱", | |
| "market": "市場", | |
| "price": "股價", | |
| "market_cap_億": "市值_億", | |
| "adv20_億": "20日均成交值_億", | |
| "pe": "本益比", | |
| "pb": "股價淨值比", | |
| "roe": "ROE", | |
| "roa": "ROA", | |
| "gross_margin": "毛利率", | |
| "operating_margin": "營業利益率", | |
| "debt_to_equity": "負債權益比", | |
| "free_cashflow": "自由現金流", | |
| "mom_12_1": "12-1月動能", | |
| "ret_6m": "6月報酬", | |
| "ret_3m": "3月報酬", | |
| "volatility": "年化波動率", | |
| "fip": "FIP", | |
| "above_ma200": "是否站上MA200", | |
| "max_drawdown": "最大回撤", | |
| "exclude_reason": "排除原因" | |
| }) | |
| num_cols = raw_display.select_dtypes(include=[np.number]).columns | |
| raw_display[num_cols] = raw_display[num_cols].round(4) | |
| passed_count = len(raw[raw["exclude_reason"] == ""]) | |
| msg = f""" | |
| ### 分析完成 | |
| 完成時間：{datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | |
| 掃描市場：{market_choice} | |
| 掃描股票數：{len(universe)} | |
| 成功取得資料：{len(raw)} | |
| 通過 AVUV-TW 條件：{passed_count} | |
| 篩選條件： | |
| - 市值：30億～500億 | |
| - 20日均成交值：大於1億 | |
| - 股價：大於10元 | |
| - ROE：至少5% | |
| - 年化波動率：低於90% | |
| - 避免負自由現金流、負營業利益率、跌破MA200、動能過弱 | |
| 總分公式： | |
| AVUV-TW總分 | |
| = 30% 價值分數 | |
| + 30% 品質分數 | |
| + 20% 動能分數 | |
| + 10% FIP分數 | |
| + 10% 低波動分數 | |
| """ | |
| if ranking.empty: | |
| msg += "\n\n目前沒有股票完全通過條件，可以放寬市值、成交值或 ROE 條件。" | |
| return ranking, raw_display, msg | |
| with gr.Blocks(css=CUSTOM_CSS, title="AVUV-TW 台股小型價值股排行榜") as demo: | |
| gr.HTML(""" | |
| <div class="hero"> | |
| <div class="title">AVUV-TW 台股排行榜</div> | |
| <div class="subtitle"> | |
| 篩選市值 30億～500億、20日均成交值大於1億的台股， | |
| 並依照價值、品質、動能、FIP、低波動因子製作排行榜。 | |
| </div> | |
| </div> | |
| """) | |
| with gr.Column(): | |
| with gr.Group(elem_classes="control-card"): | |
| market_choice = gr.Radio( | |
| choices=["上市", "上櫃", "上市+上櫃"], | |
| value="上市+上櫃", | |
| label="選擇市場" | |
| ) | |
| max_scan_count = gr.Slider( | |
| minimum=20, | |
| maximum=1000, | |
| value=200, | |
| step=20, | |
| label="最多掃描股票數" | |
| ) | |
| top_n = gr.Slider( | |
| minimum=5, | |
| maximum=100, | |
| value=30, | |
| step=5, | |
| label="排行榜顯示前 N 名" | |
| ) | |
| run_btn = gr.Button("開始製作排行榜", variant="primary") | |
| gr.Markdown(""" | |
| **固定條件** | |
| 市值：30億～500億 | |
| 20日均成交值：大於1億 | |
| 股價：大於10元 | |
| ROE：至少5% | |
| 年化波動率：小於90% | |
| """) | |
| with gr.Group(elem_classes="result-card"): | |
| message_output = gr.Markdown() | |
| with gr.Tabs(): | |
| with gr.Tab("排行榜"): | |
| ranking_output = gr.Dataframe( | |
| label="AVUV-TW 小型價值股排行榜", | |
| interactive=False, | |
| wrap=False | |
| ) | |
| with gr.Tab("原始資料"): | |
| raw_output = gr.Dataframe( | |
| label="原始分析資料與排除原因", | |
| interactive=False, | |
| wrap=False | |
| ) | |
| run_btn.click( | |
| fn=run_strategy, | |
| inputs=[market_choice, max_scan_count, top_n], | |
| outputs=[ranking_output, raw_output, message_output] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |