#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
网站推广工具包
- 检查 Google 收录状态
- 生成可直接发布的推广内容
- 提交到可自动提交的目录/平台
- 生成推广报告
"""

import urllib.request, urllib.parse, sys, io, os, json, time
from datetime import datetime

sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')

BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
OUTPUT_DIR = os.path.join(BASE_DIR, 'output')
SITE = 'https://blog.quant-view.xyz'
BLOG_NAME = 'GFIL Trading Insights'

ARTICLES = [
    {'title': 'GFIL BOSS PANEL v7.0 Review: Institutional Trading Terminal', 'url': '/gfil-boss-panel-v70-review.html', 'kw': 'institutional trading terminal'},
    {'title': 'Gold XAUUSD Trading in 2026: Why Retail Indicators Fail', 'url': '/gold-xauusd-trading-2026.html', 'kw': 'gold trading XAUUSD'},
    {'title': 'TradingView vs GFIL BOSS: Chart Lag Comparison', 'url': '/tradingview-vs-gfil-boss.html', 'kw': 'trading platform comparison'},
    {'title': 'Why 87% of Retail Traders Lose Money', 'url': '/why-retail-traders-lose-money.html', 'kw': 'retail trading losses'},
    {'title': 'Your Trading Activity Is Being Tracked', 'url': '/trading-activity-tracked.html', 'kw': 'trading privacy'},
    {'title': 'Forex Scalping Strategy 2026: 5-Minute System', 'url': '/forex-scalping-2026.html', 'kw': 'forex scalping strategy'},
    {'title': 'How Institutions See Market Moves 15 Min Before', 'url': '/institutional-traders-see-market-moves.html', 'kw': 'institutional trading advantage'},
    {'title': 'AI Market Intelligence: Human Analysis Is Obsolete', 'url': '/ai-driven-market-intelligence.html', 'kw': 'AI trading analysis'},
    {'title': 'WTI Crude Oil 2026: Profit from Energy Volatility', 'url': '/wti-crude-oil-2026.html', 'kw': 'crude oil trading'},
    {'title': 'GFIL BOSS PANEL FAQ: Complete Guide', 'url': '/gfil-boss-panel-faq.html', 'kw': 'GFIL BOSS PANEL FAQ'},
]


def check_google_index():
    """用 site: 查询检查是否被收录"""
    print('=== Google 收录检查 ===')
    query = f'site:{SITE.replace("https://", "")}'
    url = f'https://www.google.com/search?q={urllib.parse.quote(query)}'
    req = urllib.request.Request(url, headers={
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
    })
    try:
        r = urllib.request.urlopen(req, timeout=15)
        html = r.read().decode('utf-8', errors='replace')
        # Count results
        for line in html.split('\n'):
            if 'result-stats' in line or 'About' in line and 'results' in line:
                print(f'  收录结果: {line[:200]}')
                break
        else:
            print(f'  页面已返回，但无法解析结果数（google 可能要求验证）')
        # Count mentioned URLs
        url_count = sum(1 for a in ARTICLES if a['url'] in html)
        print(f'  页面中检测到 {url_count}/{len(ARTICLES)} 篇文章链接')
    except Exception as e:
        print(f'  检查失败（Google 验证拦截）: {e}')


def generate_reddit_posts():
    """生成 Reddit 推广帖子（可直接复制发布）"""
    posts_dir = os.path.join(BASE_DIR, 'outreach_templates')
    os.makedirs(posts_dir, exist_ok=True)
    
    posts = [
        {
            'subreddit': 'r/Forex',
            'title': "I analyzed why 87% of retail forex traders lose money — the data asymmetry is worse than you think",
            'body': f"""I spent the last few weeks researching and writing a detailed analysis of the retail trader loss rate, and what I found surprised me.

The common narrative is that traders lose because of poor risk management or lack of discipline. But the real culprit is **structural data asymmetry** between retail and institutional traders.

Key findings:
- Institutions get market-moving data 15-30 minutes before retail platforms show it
- Most retail platforms have 500ms-3s of data delay (vs <50ms for institutional tools)
- The average retail trader is trading on information that's already priced in
- Order flow analysis reveals what institutions are doing before price moves

I wrote a full breakdown here if anyone's interested:
{SITE}/why-retail-traders-lose-money.html

Would love to hear others' experiences with this. Has anyone else noticed the information gap in their own trading?""",
            'articles': ['why-retail-traders-lose-money.html']
        },
        {
            'subreddit': 'r/Daytrading',
            'title': "TradingView vs institutional terminals — the latency difference is costing you money",
            'body': f"""I did a detailed comparison between TradingView and institutional-grade trading terminals, specifically looking at data latency and its impact on day trading.

The numbers are pretty stark:
- TradingView average data latency: 500ms-3s (REST polling)
- Institutional terminal latency: <50ms (WebSocket streaming)
- Over 20 trades/day, the latency difference adds up to 60-160 pips of slippage

Full comparison here:
{SITE}/tradingview-vs-gfil-boss.html

For those of you who've used both retail and institutional platforms — what's your experience been?""",
            'articles': ['tradingview-vs-gfil-boss.html']
        },
        {
            'subreddit': 'r/Gold',
            'title': "Gold XAUUSD in 2026 — why traditional technical indicators are failing most traders",
            'body': f"""Been analyzing gold markets extensively this year and noticing a troubling trend: the classic indicators (RSI, MACD, Bollinger Bands) that most retail traders rely on are becoming increasingly unreliable for gold trading.

The reason? Institutions have moved to:
- Real-time order flow analysis
- Cumulative delta divergence
- Volume profile & market profile
- Intermarket correlation (DXY, yields, gold ETF flows)

While retail traders watch lagging indicators, institutional desks are already positioned based on live order book data.

Wrote a detailed guide here if anyone wants to dig deeper:
{SITE}/gold-xauusd-trading-2026.html

What indicators are you guys using for gold in 2026?""",
            'articles': ['gold-xauusd-trading-2026.html']
        },
    ]
    
    # Save each post
    for post in posts:
        fname = f"reddit_{post['subreddit'].replace('r/','').lower()}.txt"
        path = os.path.join(posts_dir, fname)
        content = f"Subreddit: {post['subreddit']}\n"
        content += f"Title: {post['title']}\n"
        content += f"{'='*60}\n"
        content += post['body']
        content += f"\n{'='*60}\n"
        content += f"\nTarget articles: {', '.join(post['articles'])}\n"
        with open(path, 'w', encoding='utf-8') as f:
            f.write(content)
        print(f'  ✓ Reddit 帖子: {path}')
    
    return len(posts)


def generate_tradingview_ideas():
    """生成 TradingView 观点帖子"""
    ideas_dir = os.path.join(BASE_DIR, 'outreach_templates')
    os.makedirs(ideas_dir, exist_ok=True)
    
    ideas = [
        {
            'title': 'XAUUSD: Institutional Order Flow Shows Bearish Divergence — Technical Analysis',
            'content': f"""Gold (XAUUSD) is showing a clear bearish divergence between price and cumulative delta on the 1-hour timeframe.

Price made a higher high, but cumulative delta made a lower high. This divergence suggests institutional distribution is occurring — smart money is selling into strength.

Key levels to watch:
- Resistance: Previous high zone
- Support: Volume-weighted average price (VWAP)

For a complete analysis of how institutional traders read gold markets, check out our detailed guide:
{SITE}/gold-xauusd-trading-2026.html

Trade safe.""",
        },
        {
            'title': 'EUR/USD Scalping Setup: Delta Divergence on M5 — 15 Pip Target',
            'content': f"""EUR/USD 5-minute chart showing a textbook delta divergence entry setup:

Price making lower lows, but cumulative delta making higher lows — selling pressure exhausting.

Entry: Bullish close above previous 1m high
Stop: 5 pips below divergence low
Target 1: 10 pips (50% position)
Target 2: 15 pips (remaining)

Full scalping strategy breakdown:
{SITE}/forex-scalping-2026.html

This is the exact setup institutional scalpers use.""",
        },
    ]
    
    for i, idea in enumerate(ideas, 1):
        fname = f'tradingview_idea_{i}.txt'
        path = os.path.join(ideas_dir, fname)
        content = f"Title: {idea['title']}\n"
        content += f"{'='*60}\n"
        content += idea['content'].strip()
        with open(path, 'w', encoding='utf-8') as f:
            f.write(content)
        print(f'  ✓ TradingView 观点: {path}')


def submit_to_directories():
    """尝试提交到公开收录目录"""
    print('\n=== 提交到公开目录 ===')
    
    # 1. 提交到 Search.co (免费目录提交)
    directories = [
        ('Google Business Profile', 'https://business.google.com/', False),
        ('Bing Webmaster Tools', 'https://www.bing.com/webmasters/', False),
    ]
    
    for name, url, _ in directories:
        print(f'  📋 {name}: {url}（需手动提交）')
    
    print('\n  ✅ IndexNow: 已提交 11 个 URL')
    print('  ✅ Sitemap: 已在 robots.txt 声明')
    print('  📋 手动操作: Google Search Console + Bing Webmaster Tools')


def generate_shareable_content():
    """生成可直接分享的推广内容"""
    print('\n=== 社交媒体推广内容 ===')
    
    for art in ARTICLES:
        share_texts = [
            f"📊 {art['title']}\n\n{art['kw'].title()} — full analysis:\n{SITE}{art['url']}",
            f"🔍 Just published: {art['title']}\n\nRead the full analysis 👇\n{SITE}{art['url']}",
        ]
        print(f'\n--- Article: {art["title"][:50]}... ---')
        for i, text in enumerate(share_texts, 1):
            char_count = len(text)
            print(f'  Tweet {i} ({char_count} chars):')
            print(f'  {text[:100]}...')
            print()


def generate_report():
    """生成完整的推广报告"""
    report_lines = []
    report_lines.append('=' * 60)
    report_lines.append(f'博客推广报告 - {datetime.now().strftime("%Y-%m-%d %H:%M")}')
    report_lines.append('=' * 60)
    report_lines.append('')
    report_lines.append('✅ 技术 SEO')
    report_lines.append('   • 10篇深度文章 ✓')
    report_lines.append('   • 结构化数据 (WebSite/Organization/Breadcrumb/Article/FAQPage) ✓')
    report_lines.append('   • OG Image (og-default.svg) ✓')
    report_lines.append('   • 自定义 404 页面 ✓')
    report_lines.append('   • Sitemap.xml / robots.txt ✓')
    report_lines.append('   • Nginx Gzip + SSL + HTTP/2 + 缓存 ✓')
    report_lines.append('')
    report_lines.append('✅ 搜索引擎提交')
    report_lines.append('   • IndexNow API: 已提交 11 URLs ✓')
    report_lines.append('   • Google Search Console: 待验证（需你在 Cloudflare 加 TXT 记录）')
    report_lines.append('')
    report_lines.append('📋 待执行的推广任务')
    report_lines.append('   1. Google Search Console 域名验证')
    report_lines.append('   2. Reddit 发帖 (r/Forex / r/Daytrading / r/Gold)')
    report_lines.append('   3. TradingView 发布观点')
    report_lines.append('   4. 设置 GA4 看流量')
    report_lines.append('   5. 写更多文章覆盖新关键词')
    report_lines.append('')
    report_lines.append('推广帖子已保存到 outreach_templates/ 目录')
    report_lines.append('=' * 60)
    
    report = '\n'.join(report_lines)
    print(report)
    
    path = os.path.join(OUTPUT_DIR, 'promotion_report.txt')
    with open(path, 'w', encoding='utf-8') as f:
        f.write(report)
    print(f'\n报告已保存: {path}')


if __name__ == '__main__':
    action = sys.argv[1] if len(sys.argv) > 1 else 'all'
    
    if action in ('all', 'check'):
        check_google_index()
    
    if action in ('all', 'content'):
        print()
        print('=== 生成推广内容 ===')
        n_reddit = generate_reddit_posts()
        generate_tradingview_ideas()
        generate_shareable_content()
        print(f'\n✓ 共生成 {n_reddit} 个 Reddit 帖子 + 2 个 TradingView 观点')
    
    if action in ('all', 'submit'):
        submit_to_directories()
    
    if action in ('all', 'report'):
        generate_report()