import yfinance as yf import pandas as pd import numpy as np import requests from datetime import datetime import os import re import urllib.request import urllib.parse import xml.etree.ElementTree as ET from config import TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID from scanner_common import ( calculate_rsi, detect_bullish_engulfing, detect_hammer, detect_liquidity_sweep, detect_volume_spike, detect_volume_ratio, detect_rsi_bullish_divergence, drop_incomplete_last_bar, make_verdict, regime_min_score, rolling_high, ) # ========================================================= # CONFIGURATION # ========================================================= REGIME_FILTER_MODE = "block" SIGNAL_LOOKBACK_DAYS = 3 # ฝั่ง US ใช้ 3 วัน (ตามระบบเดิม) MIN_SCORE_BASE = 2 BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DASHBOARD_FILE = os.path.join(BASE_DIR, "dashboard.html") TUNNEL_LOG = os.path.join(BASE_DIR, "tunnel.log") # ========================================================= # UNIVERSE: S&P 500 / NASDAQ QUALITY BLUE CHIPS # ========================================================= UNIVERSE_US = [ # เทคโนโลยี (Tech) "AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "TSLA", "NFLX", "AMD", "CRM", "ADBE", "INTC", "QCOM", "AVGO", "AMAT", "LRCX", # ค้าปลีกและอุปโภค (Consumer) "COST", "WMT", "NKE", "SBUX", "MCD", "DIS", "KO", "PEP", "PG", # การเงิน (Finance) "V", "MA", "JPM", "BAC", "PYPL", # การแพทย์ (Healthcare) "LLY", "UNH", "JNJ", "PFE", "ABBV", "MRK", # อุตสาหกรรมและพลังงาน (Industrial & Energy) "CAT", "BA", "DE", "XOM", "CVX" ] # ========================================================= # REGIME CALCULATION (S&P 500 vs EMA200) # ========================================================= def check_sp500_regime(): try: idx = yf.Ticker("^GSPC").history(period="1y") idx, _ = drop_incomplete_last_bar(idx, "US") if idx.empty: return "UNKNOWN", 0, 0, 0 idx['EMA200'] = idx['Close'].ewm(span=200, adjust=False).mean() latest_close = idx['Close'].iloc[-1] latest_ema200 = idx['EMA200'].iloc[-1] pct_diff = ((latest_close - latest_ema200) / latest_ema200) * 100 is_bull = latest_close > latest_ema200 return ("BULL", latest_close, latest_ema200, pct_diff) if is_bull else ("BEAR", latest_close, latest_ema200, pct_diff) except Exception: return "UNKNOWN", 0, 0, 0 # ========================================================= # NEWS FETCHER & TECHNICAL DIAGNOSIS # ========================================================= def fetch_us_news(symbol): """Fetch English financial news headlines via Google News RSS""" try: query = urllib.parse.quote(f"{symbol} stock") url = f"https://news.google.com/rss/search?q={query}&hl=en-US&gl=US&ceid=US:en" req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)'}) with urllib.request.urlopen(req, timeout=5) as resp: xml_data = resp.read() root = ET.fromstring(xml_data) items = root.findall('./channel/item') headlines = [] for it in items[:3]: title = it.find('title').text title = re.sub(r"\s*-\s*[^-]+$", "", title) headlines.append(title.strip()) return " | ".join(headlines) if headlines else "No significant negative news" except Exception: return "Market conditions normal" def ask_ai_us(symbol, price, drawdown, rsi, news_text, sweep=False, pa=False, ema=False, score=0, volume_spike=False, rsi_divergence=False, vol_ratio=1.0): """สร้างคำอธิบายแบบ rule-based (ไม่ใช้ AI) — ผลคงที่ทุกครั้ง""" return make_verdict(drawdown, rsi, sweep, pa, ema, score, news_text, volume_spike, rsi_divergence, vol_ratio) def send_telegram_alert(text): url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage" payload = { "chat_id": TELEGRAM_CHAT_ID, "text": text, "parse_mode": "Markdown" } try: res = requests.post(url, data=payload, timeout=10) return res.json() except Exception as e: print(f"Error sending telegram: {e}") return None def get_live_tunnel_url(): if os.path.exists(TUNNEL_LOG): try: with open(TUNNEL_LOG, "r") as f: matches = re.findall(r"https://[a-zA-Z0-9-]+\.trycloudflare\.com", f.read()) if matches: return matches[-1] except Exception: pass return None # ========================================================= # DASHBOARD GENERATOR (US) # ========================================================= SECTOR_MAP_US = { "AAPL": "เทคโนโลยี (Apple)", "MSFT": "ซอฟต์แวร์ (Microsoft)", "GOOGL": "อินเทอร์เน็ต (Alphabet)", "AMZN": "อีคอมเมิร์ซ (Amazon)", "META": "โซเชียลมีเดีย (Meta)", "NVDA": "ชิป AI (Nvidia)", "TSLA": "ยานยนต์ไฟฟ้า (Tesla)", "NFLX": "สตรีมมิ่ง (Netflix)", "AMD": "ชิปประมวลผล (AMD)", "CRM": "CRM (Salesforce)", "ADBE": "ซอฟต์แวร์ครีเอทีฟ (Adobe)", "INTC": "ชิปเซมิคอนดักเตอร์ (Intel)", "QCOM": "ชิปมือถือ (Qualcomm)", "AVGO": "ชิป (Broadcom)", "AMAT": "อุปกรณ์เซมิคอนดักเตอร์ (Applied)", "LRCX": "อุปกรณ์เซมิคอนดักเตอร์ (Lam)", "COST": "ค้าปลีก (Costco)", "WMT": "ค้าปลีก (Walmart)", "NKE": "เครื่องกีฬา (Nike)", "SBUX": "กาแฟ (Starbucks)", "MCD": "ฟาสต์ฟู้ด (McDonald's)", "DIS": "มีเดีย/บันเทิง (Disney)", "KO": "เครื่องดื่ม (Coca-Cola)", "PEP": "เครื่องดื่ม/อาหาร (PepsiCo)", "PG": "อุปโภค (Procter & Gamble)", "V": "การเงินดิจิทัล (Visa)", "MA": "การเงินดิจิทัล (Mastercard)", "JPM": "ธนาคาร (JPMorgan)", "BAC": "ธนาคาร (Bank of America)", "PYPL": "การเงินดิจิทัล (PayPal)", "LLY": "ยา (Eli Lilly)", "UNH": "ประกันสุขภาพ (UnitedHealth)", "JNJ": "ยา/เวชภัณฑ์ (J&J)", "PFE": "ยา (Pfizer)", "ABBV": "ยา (AbbVie)", "MRK": "ยา (Merck)", "CAT": "เครื่องจักรหนัก (Caterpillar)", "BA": "อากาศยาน (Boeing)", "DE": "เครื่องจักรเกษตร (Deere)", "XOM": "พลังงาน (ExxonMobil)", "CVX": "พลังงาน (Chevron)" } APPLE_ACCENTS = [ ("from-emerald-400 to-teal-500", "bg-emerald-50 text-emerald-700 border-emerald-200"), ("from-sky-400 to-blue-500", "bg-sky-50 text-sky-700 border-sky-200"), ("from-indigo-400 to-violet-500", "bg-indigo-50 text-indigo-700 border-indigo-200"), ("from-purple-400 to-fuchsia-500", "bg-purple-50 text-purple-700 border-purple-200"), ("from-amber-400 to-orange-500", "bg-amber-50 text-amber-700 border-amber-200"), ("from-rose-400 to-pink-500", "bg-rose-50 text-rose-700 border-rose-200") ] def generate_dashboard_us_html(regime, spx_close, spx_ema, pct_diff, grade_a, watchlist): timestamp_str = datetime.now().strftime("%d %b %Y | %H:%M น.") count_top = len([s for s in grade_a if s['score'] >= 4]) cards_html = "" for idx, s in enumerate(grade_a): clean_sym = s['symbol'] sector = SECTOR_MAP_US.get(s['symbol'], "S&P 500 Blue Chip") score = s['score'] is_top = (score >= 4) grad_color, pill_color = APPLE_ACCENTS[idx % len(APPLE_ACCENTS)] if is_top: star_badge = """ ★ สัญญาณครบ 4/4 (Prime Setup) """ card_ring = "ring-2 ring-amber-400/50 shadow-[0_12px_36px_rgba(245,158,11,0.12)]" else: star_badge = f""" ● ผ่าน {score}/4 สัญญาณ """ card_ring = "border border-white/60 shadow-[0_8px_30px_rgba(0,0,0,0.04)]" cards_html += f"""
{clean_sym[:4]}

{clean_sym}

{sector}

${s['price']:.2f}
-{s['drawdown']:.1f}% RSI {s['rsi']:.1f}
{star_badge}
{'✓' if s['sweep'] else '—'} SMC Sweep
{'✓' if s['pa'] else '—'} Price Action
{'✓' if s['ema'] else '—'} เหนือ EMA9
{'✓' if s.get('volume_spike') else '—'} Volume {s.get('vol_ratio', 1.0):.1f}x
จุดเข้า
${s['price']:.2f}
ตัดขาดทุน
${s.get('stop_loss', 0):.2f}
{s.get('sl_pct', 0):.1f}%
เป้าขาย
${s.get('take_profit', 0):.2f}
+{s.get('tp_pct', 0):.1f}%
🧠 วิเคราะห์ตามสัญญาณเทคนิค:

{s.get('ai_verdict', 'ย่อตัวตามภาวะตลาดรวม')}

📰 ข่าวล่าสุด: {s.get('news_summary', '')[:90]}...
{'
📈 RSI Divergence detected — ขาลงอ่อนแรง มีโอกาสกลับตัว
' if s.get('rsi_divergence') else ''}
""" watchlist_html = "" for w in watchlist: clean_w = w['symbol'] w_sector = SECTOR_MAP_US.get(w['symbol'], 'S&P 500') watchlist_html += f"""
{clean_w[:4]}
{clean_w}
{w_sector}
${w['price']:.2f}
-{w['drawdown']:.1f}% (RSI {w['rsi']:.1f})
""" empty_html = """
🛡️

ยังไม่มีหุ้นเกรด A+ ครบทุกเงื่อนไข

ระบบสแกน S&P 500 แล้ว ไม่พบหุ้นที่เข้าเกณฑ์

""" regime_color = "bg-emerald-500 shadow-emerald-500" if regime == "BULL" else "bg-amber-500 shadow-amber-500" regime_text = f"BULL (+{pct_diff:.1f}% เหนือ EMA200)" if regime == "BULL" else f"BEAR ({pct_diff:.1f}% ต่ำกว่า EMA200)" full_html = f""" S&P 500 Radar Terminal
🇺🇸 ตลาดหุ้นอเมริกา (S&P 500) 🇹🇭 ตลาดหุ้นไทย (SET100)
S&P 500 Terminal
🕒 {timestamp_str}

S&P 500 Radar Terminal

คัดกรองหุ้นอเมริกาชั้นนำ ย่อตัวลึก + SMC Sweep + แท่งกลับตัว + วิเคราะห์ตามสัญญาณ

S&P 500
{regime_text}
หุ้นเกรด A+
{len(grade_a)} ตัว
ผ่านเกณฑ์ครบ: {count_top} ตัว
Watchlist
{len(watchlist)} ตัว
ย่อตัวลึก รอสัญญาณ
S&P 500
{spx_close:.2f} จุด
EMA200: {spx_ema:.2f}
{cards_html if grade_a else empty_html}

👀 Watchlist หุ้นคุณภาพในโซนถูก

{len(watchlist)} ตัว
{watchlist_html}
""" with open(DASHBOARD_FILE, "w", encoding="utf-8") as f: f.write(full_html) print(f"✅ Dashboard saved: {DASHBOARD_FILE}") # ========================================================= # MAIN SCANNER LOGIC # ========================================================= def main(): print("=====================================================") print("🚀 เริ่มสแกนตลาดหุ้นอเมริกา (S&P 500)...") print(f"🕒 เวลา: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print("=====================================================") regime, spx_close, spx_ema, pct_diff = check_sp500_regime() print(f"📊 สภาวะ S&P 500: {regime} (SPX: {spx_close:.2f} / EMA200: {spx_ema:.2f})") grade_a = [] watchlist = [] min_score = regime_min_score(regime, base_score=MIN_SCORE_BASE, mode=REGIME_FILTER_MODE) if min_score >= 90: print(f"⚠️ S&P 500 เป็น {regime} -> ระงับการแจ้งเตือนทั้งหมดวันนี้") elif min_score != MIN_SCORE_BASE: print(f"⚠️ S&P 500 เป็น {regime} -> ยกระดับเกณฑ์เป็น {min_score}/3") else: print(f"ℹ️ เกณฑ์แจ้งเตือนขั้นต่ำ: {min_score}/4 สัญญาณ") warned_bar = False for sym in UNIVERSE_US: try: t = yf.Ticker(sym) df = t.history(period="1y") df, note = drop_incomplete_last_bar(df, "US") if note and not warned_bar: print(f"ℹ️ {note}") warned_bar = True if df.empty or len(df) < 30: continue latest_close = df['Close'].iloc[-1] df['52W_High'] = rolling_high(df, window=250, min_periods=30) high_52w = df['52W_High'].iloc[-1] drawdown = ((latest_close - high_52w) / high_52w) * 100 df['RSI'] = calculate_rsi(df['Close']) latest_rsi = df['RSI'].iloc[-1] if (drawdown <= -10.0 and latest_rsi <= 45.0) or (drawdown <= -8.0 and latest_rsi <= 38.0): df['EMA9'] = df['Close'].ewm(span=9, adjust=False).mean() df['EMA50'] = df['Close'].ewm(span=50, adjust=False).mean() ema_pass = bool(latest_close > df['EMA9'].iloc[-1]) lb = SIGNAL_LOOKBACK_DAYS sweep_pass = bool(detect_liquidity_sweep(df, lookback=10).iloc[-lb:].any()) pa_series = detect_hammer(df, wick_mult=1.8) | detect_bullish_engulfing(df) pa_pass = bool(pa_series.iloc[-lb:].any()) vol_spike = detect_volume_spike(df, window=20, threshold=2.0) vol_ratio = detect_volume_ratio(df, window=20) rsi_div = detect_rsi_bullish_divergence(df) score = int(sweep_pass) + int(pa_pass) + int(ema_pass) + int(vol_spike) stop_loss = float(df['Low'].iloc[-3:].min()) * 0.99 take_profit = max(float(df['EMA50'].iloc[-1]), float(latest_close) * 1.08) news_text = fetch_us_news(sym) stock_data = { "symbol": sym, "price": latest_close, "drawdown": abs(drawdown), "rsi": latest_rsi, "sweep": sweep_pass, "pa": pa_pass, "ema": ema_pass, "volume_spike": vol_spike, "vol_ratio": vol_ratio, "rsi_divergence": rsi_div, "score": score, "stop_loss": stop_loss, "take_profit": take_profit, "sl_pct": ((stop_loss - latest_close) / latest_close) * 100, "tp_pct": ((take_profit - latest_close) / latest_close) * 100, "news_summary": news_text } if score >= min_score: print(f"🔎 พบ {sym} (คะแนน {score}/4)") stock_data["ai_verdict"] = ask_ai_us( sym, latest_close, abs(drawdown), latest_rsi, news_text, sweep_pass, pa_pass, ema_pass, score, vol_spike, rsi_div, vol_ratio) grade_a.append(stock_data) else: stock_data["ai_verdict"] = "โซนของถูก รอยืนยันสัญญาณกลับตัว" watchlist.append(stock_data) except Exception as e: print(f"Error processing {sym}: {e}") continue grade_a = sorted(grade_a, key=lambda x: (x['score'], -x['drawdown']), reverse=True) watchlist = sorted(watchlist, key=lambda x: (x['score'], -x['drawdown']), reverse=True)[:6] generate_dashboard_us_html(regime, spx_close, spx_ema, pct_diff, grade_a, watchlist) if grade_a: msg = f"🇺🇸 *แจ้งเตือนเรดาร์หุ้นอเมริกา (S&P 500) เกรด A+*\n" msg += f"🗓️ {datetime.now().strftime('%d/%m/%Y')} | สภาวะ SPX: *{regime}*\n" msg += f"------------------------------------\n" for s in grade_a[:4]: clean_name = s['symbol'] score_text = "ผ่านครบ 4 สัญญาณ" if s['score'] >= 4 else f"ผ่าน {s['score']}/4 สัญญาณ" extras = [] if s.get('volume_spike'): extras.append(f"🔥 Vol {s['vol_ratio']:.1f}x") if s.get('rsi_divergence'): extras.append("📈 RSI Div") extra_txt = " | ".join(extras) msg += f"🟢 *{clean_name}* (ราคา ${s['price']:.2f})\n" msg += f"• ย่อตัว: *-{s['drawdown']:.1f}%* | RSI: *{s['rsi']:.1f}*\n" msg += f"• สัญญาณ: *{score_text}*" if extra_txt: msg += f" ({extra_txt})" msg += "\n" msg += f"• 🎯 *จุดเข้า:* ${s['price']:.2f} | 🛑 *SL:* ${s['stop_loss']:.2f} ({s['sl_pct']:.1f}%)\n" msg += f"• 🏆 *เป้าขาย:* ${s['take_profit']:.2f} (+{s['tp_pct']:.1f}%)\n" msg += f"• 🧠 *วิเคราะห์สัญญาณ:* _{s['ai_verdict']}_\n\n" live_url = get_live_tunnel_url() if live_url: msg += f"🌐 *เปิดดู Dashboard ฉบับเต็ม:*\n{live_url}/dashboard.html\n" else: msg += "_(หา URL ของ tunnel ไม่เจอ — ตรวจสอบว่า cloudflared ทำงานอยู่หรือไม่)_\n" send_telegram_alert(msg) print(f"✅ ส่งแจ้งเตือนหุ้นอเมริกา {len(grade_a)} ตัว เข้า Telegram สำเร็จ!") elif min_score >= 90: print(f"ℹ️ S&P 500 อยู่ใต้ EMA200 (BEAR) — ระงับการแจ้งเตือนวันนี้") else: print("ℹ️ วันนี้ไม่มีหุ้นอเมริกาเข้าเกณฑ์เกรด A+") print(f"\n📊 สรุป: {len(grade_a)} หุ้นเกรด A+ | {len(watchlist)} หุ้น Watchlist") if __name__ == "__main__": main()