{"repo": "altusrossouw/reddit-stonks", "files": {"app.py": {"9572f1123a0c8c29495e0ee8cf3d5c5f8cc5008c": {"t": 1785745407.607817, "n": 10760, "h": "f9006a62feee209f", "c": "#!/usr/bin/env python3\nimport asyncio\nimport json\nimport os\nimport re\nimport sys\nfrom datetime import datetime\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom fastapi import FastAPI, Query\nfrom fastapi.responses import HTMLResponse, StreamingResponse\nfrom fastapi.staticfiles import StaticFiles\n\nfrom reddit_scraper import scrape_all\nfrom stock_data import fetch_stock_data, find_european_equivalents\nfrom backtest import save_pick, compute_performance\n\nload_dotenv()\n\nDEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\nDEEPSEEK_BASE_URL = \"https://api.deepseek.com/v1\"\n\nclient = OpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL)\napp = FastAPI(title=\"Reddit Stonks\")\nTOP_N_TICKERS = 20\n\n\ndef extract_top_picks(analysis: str) -> list[str]:\n    picks: list[str] = []\n    for match in re.finditer(\n        r\"\\*\\*TOP PICK:\\s*\\$?(\\w+)\\*\\*|\\*\\*RUNNER-UP:\\s*\\$?(\\w+)\\*\\*|\\*\\*WILDCARD[^*]*:\\s*\\$?(\\w+)\\*\\*\",\n        analysis,\n    ):\n        ticker = match.group(1) or match.group(2) or match.group(3)\n        if ticker:\n            picks.append(ticker)\n    return picks\n\n\ndef parse_confidence(analysis: str) -> int:\n    m = re.search(r\"Confidence Score:\\s*(\\d+)/10\", analysis, re.IGNORECASE)\n    return int(m.group(1)) if m else 0\n\n\ndef parse_top_ticker_price(ticker: str, stock_data: dict) -> float:\n    sd = stock_data.get(ticker, {})\n    return sd.get(\"price\", 0)\n\n\ndef build_context(reddit_data: dict, stock_data: dict, euro: bool = False) -> str:\n    ticker_counts = reddit_data[\"ticker_counts\"]\n    top_tickers = list(ticker_counts.keys())[:TOP_N_TICKERS]\n    trends = reddit_data.get(\"trends\", {})\n\n    lines = [\n        f\"Analysis Date: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}\",\n        f\"Total Reddit Posts Analyzed: {len(reddit_data['posts'])}\",\n        \"\",\n        \"=== STOCK DATA (with MOMENTUM TREND) ===\",\n    ]\n\n    for ticker in top_tickers:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        mentions = ticker_counts.get(ticker, 0)\n        trend = trends.get(ticker, \"steady\")\n        cap_str = f\"${sd['market_cap']/1e9:.1f}B\" if sd[\"market_cap\"] else \"N/A\"\n        target_str = f\"${sd['target_mean']:.2f}\" if sd.get(\"target_mean\") else \"N/A\"\n        short_str = f\"{sd['short_pct']*100:.1f}%\" if sd.get(\"short_pct\") else \"N/A\"\n\n        lines.append(\n            f\"${ticker} ({sd['short_name']}): \"\n            f\"Price=${sd['price']:.2f}, \"\n            f\"MktCap={cap_str}, \"\n            f\"Sector={sd['sector']}, \"\n            f\"1WkChange={sd['week_change_pct']:+.2f}%, \"\n            f\"P/E={sd.get('pe_ratio', 'N/A')}, \"\n            f\"FwdP/E={sd.get('forward_pe', 'N/A')}, \"\n            f\"Beta={sd.get('beta', 'N/A')}, \"\n            f\"50dMA={sd.get('fifty_day_avg', 'N/A')}, \"\n            f\"200dMA={sd.get('two_hundred_day_avg', 'N/A')}, \"\n            f\"Recommendation={sd['recommendation']}, \"\n            f\"AnalystTarget={target_str}, \"\n            f\"ShortFloat={short_str}, \"\n            f\"InstOwnership={sd.get('inst_ownership', 'N/A')}, \"\n            f\"VolRatio={sd['volume_ratio']}, \"\n            f\"RedditMentions={mentions}, \"\n            f\"MentionsTrend={trend} (rising=accelerating hype, falling=fading interest, new=first appearance, steady=same level)\"\n        )\n\n    trending_up = [t for t in top_tickers if trends.get(t) == \"rising\"]\n    trending_down = [t for t in top_tickers if trends.get(t) == \"falling\"]\n    if trending_up:\n        lines.append(f\"\\nRISING mentions (more hype this week vs last): {', '.join('$'+t for t in trending_up)}\")\n    if trending_down:\n        lines.append(f\"FALLING mentions (less hype this week vs last): {', '.join('$'+t for t in trending_down)}\")\n\n    lines.append(\"\")\n    lines.append(\"=== REDDIT MENTION RANKING ===\")\n    for ticker, count in list(ticker_counts.items())[:TOP_N_TICKERS]:\n        trend_icon = {\"rising\": \"↑\", \"falling\": \"↓\", \"new\": \"★\", \"steady\": \"→\", \"gone\": \"✗\"}.get(trends.get(ticker, \"\"), \"\")\n        lines.append(f\"  ${ticker}: {count} mentions {trend_icon}\")\n\n    if euro:\n        lines.append(\"\")\n        lines.append(\"=== NOTE ===\")\n        lines.append(\"Include European exchange equivalents section in your response.\")\n\n    return \"\\n\".join(lines)\n\n\nasync def run_analysis(context: str) -> str:\n    system_prompt = (\n        \"You are a senior quantitative analyst at a top hedge fund. \"\n        \"Your specialty is combining social media sentiment analysis with \"\n        \"fundamental and technical data to identify short-term trading opportunities. \"\n        \"Output your analysis in clear sections with specific reasoning. \"\n        \"Be concise and data-driven.\\n\\n\"\n        \"CRITICAL: A stock with RISING mentions + strong fundamentals is a buy signal. \"\n        \"A stock with FALLING mentions (fading hype) is a sell signal — do NOT pick these. \"\n        \"A stock with RISING mentions but weak fundamentals is a hype trap — be skeptical. \"\n        \"Weight fundamentals (P/E, beta, analyst consensus) at least as heavily as Reddit sentiment.\"\n    )\n\n    user_prompt = f\"\"\"Analyze the following data and determine which single stock has the highest probability of delivering the best return over the NEXT 7 DAYS (1 week).\n\nConsider:\n1. Reddit hype/momentum\n2. Recent price action and technical setup\n3. Valuation (P/E, forward P/E)\n4. Short squeeze potential\n5. Volume anomalies\n6. Analyst sentiment and price targets\n7. Sector momentum\n\nOutput format:\n\n**TOP PICK: $TICKER**\n- Rationale: (2-3 sentences)\n\n**RUNNER-UP: $TICKER**\n- Rationale: (1-2 sentences)\n\n**WILDCARD (High Risk/Reward): $TICKER**\n- Rationale: (1-2 sentences)\n\n**Risk Factors to Watch:**\n- (2-3 bullet points)\n\n**Confidence Score: X/10**\n- (1 sentence)\n\nDATA:\n{context}\"\"\"\n\n    loop = asyncio.get_event_loop()\n    try:\n        response = await loop.run_in_executor(\n            None,\n            lambda: client.chat.completions.create(\n                model=\"deepseek-v4-flash\",\n                messages=[\n                    {\"role\": \"system\", \"content\": system_prompt},\n                    {\"role\": \"user\", \"content\": user_prompt},\n                ],\n                temperature=0.3,\n                max_tokens=1200,\n            ),\n        )\n        return response.choices[0].message.content or \"(no response)\"\n    except Exception as e:\n        return f\"Error calling Deepseek API: {e}\"\n\n\nasync def send_event(msg_type: str, data: dict):\n    yield f\"data: {json.dumps({'type': msg_type, **data})}\\n\\n\"\n\n\nasync def event_stream(posts_per_sub: int, euro: bool):\n    try:\n        yield f\"data: {json.dumps({'type': 'status', 'message': 'Scraping Reddit...'})}\\n\\n\"\n\n        loop = asyncio.get_event_loop()\n        reddit_data = await loop.run_in_executor(None, lambda: scrape_all(limit_per_sub=posts_per_sub))\n\n        ticker_count = len(reddit_data[\"ticker_counts\"])\n        yield f\"data: {json.dumps({'type': 'status', 'message': f'Found {ticker_count} unique tickers. Fetching stock data...'})}\\n\\n\"\n\n        top_tickers = list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]\n        stock_data = await loop.run_in_executor(None, fetch_stock_data, top_tickers)\n\n        yield f\"data: {json.dumps({'type': 'status', 'message': f'Stock data ready for {len(stock_data)} tickers. Sending to AI...'})}\\n\\n\"\n\n        stock_rows = []\n        for ticker in list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]:\n            sd = stock_data.get(ticker)\n            if not sd:\n                continue\n            stock_rows.append({\n                \"ticker\": ticker,\n                \"price\": sd[\"price\"],\n                \"currency\": sd[\"currency\"],\n                \"market_cap\": sd[\"market_cap\"],\n                \"sector\": sd[\"sector\"],\n                \"short_name\": sd[\"short_name\"],\n                \"week_change_pct\": sd[\"week_change_pct\"],\n                \"pe_ratio\": sd.get(\"pe_ratio\"),\n                \"forward_pe\": sd.get(\"forward_pe\"),\n                \"beta\": sd.get(\"beta\"),\n                \"volume_ratio\": sd[\"volume_ratio\"],\n            \"short_pct\": sd.get(\"short_pct\"),\n            \"inst_ownership\": sd.get(\"inst_ownership\"),\n            \"recommendation\": sd[\"recommendation\"],\n                \"target_mean\": sd.get(\"target_mean\"),\n                \"mentions\": reddit_data[\"ticker_counts\"].get(ticker, 0),\n                \"trend\": reddit_data.get(\"trends\", {}).get(ticker, \"steady\"),\n            })\n\n        context = build_context(reddit_data, stock_data, euro=euro)\n        analysis = await run_analysis(context)\n\n        picks = extract_top_picks(analysis)\n        top_ticker = picks[0] if len(picks) > 0 else \"\"\n        runner_ticker = picks[1] if len(picks) > 1 else \"\"\n        wildcard_ticker = picks[2] if len(picks) > 2 else \"\"\n\n        save_pick(\n            top_pick=top_ticker,\n            top_price=parse_top_ticker_price(top_ticker, stock_data),\n            runner_up=runner_ticker,\n            runner_up_price=parse_top_ticker_price(runner_ticker, stock_data),\n            wildcard=wildcard_ticker,\n            wildcard_price=parse_top_ticker_price(wildcard_ticker, stock_data),\n            confidence=parse_confidence(analysis),\n            posts_scraped=len(reddit_data[\"posts\"]),\n            tickers_found=len(reddit_data[\"ticker_counts\"]),\n            analysis=analysis,\n        )\n\n        euro_data = []\n        if euro:\n            picks = extract_top_picks(analysis)\n            for us_ticker in picks:\n                eq = await loop.run_in_executor(None, find_european_equivalents, us_ticker)\n                euro_data.append({\"us_ticker\": us_ticker, \"equivalents\": eq})\n\n        yield f\"data: {json.dumps({'type': 'result', 'stocks': stock_rows, 'analysis': analysis, 'euro': euro_data})}\\n\\n\"\n        yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n    except Exception as e:\n        yield f\"data: {json.dumps({'type': 'error', 'message': str(e)})}\\n\\n\"\n        yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n\n@app.get(\"/api/analyze\")\nasync def analyze(\n    posts: int = Query(25, ge=10, le=25),\n    euro: bool = Query(False),\n):\n    return StreamingResponse(\n        event_stream(posts, euro),\n        media_type=\"text/event-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"X-Accel-Buffering\": \"no\",\n        },\n    )\n\n\n@app.get(\"/\")\nasync def index():\n    return HTMLResponse(open(\"static/index.html\").read())\n\n\n@app.get(\"/app\")\nasync def app_page():\n    return HTMLResponse(open(\"static/app.html\").read())\n\n\n@app.get(\"/track-record\")\nasync def track_record_page():\n    return HTMLResponse(open(\"static/track-record.html\").read())\n\n\n@app.get(\"/api/track-record\")\nasync def track_record_api():\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(None, compute_performance)\n\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n"}, "d6fd3bd3400f7d2a5ad8be10176a2bca3a8f2aa8": {"t": 1785752902.2421706, "n": 10647, "h": "c59217cb123416c9", "c": "#!/usr/bin/env python3\nimport asyncio\nimport json\nimport os\nimport re\nimport sys\nfrom datetime import datetime\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom fastapi import FastAPI, Query\nfrom fastapi.responses import HTMLResponse, StreamingResponse\nfrom fastapi.staticfiles import StaticFiles\n\nfrom reddit_scraper import scrape_all\nfrom stock_data import fetch_stock_data, find_european_equivalents\nfrom backtest import save_pick, compute_performance\n\nload_dotenv()\n\nDEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\nDEEPSEEK_BASE_URL = \"https://api.deepseek.com/v1\"\n\nclient = OpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL)\napp = FastAPI(title=\"Reddit Stonks\")\nTOP_N_TICKERS = 20\n\n\ndef extract_top_picks(analysis: str) -> list[str]:\n    picks: list[str] = []\n    for match in re.finditer(\n        r\"\\*\\*TOP PICK:\\s*\\$?(\\w+)\\*\\*|\\*\\*RUNNER-UP:\\s*\\$?(\\w+)\\*\\*|\\*\\*WILDCARD[^*]*:\\s*\\$?(\\w+)\\*\\*\",\n        analysis,\n    ):\n        ticker = match.group(1) or match.group(2) or match.group(3)\n        if ticker:\n            picks.append(ticker)\n    return picks\n\n\ndef parse_confidence(analysis: str) -> int:\n    m = re.search(r\"Confidence Score:\\s*(\\d+)/10\", analysis, re.IGNORECASE)\n    return int(m.group(1)) if m else 0\n\n\ndef parse_top_ticker_price(ticker: str, stock_data: dict) -> float:\n    sd = stock_data.get(ticker, {})\n    return sd.get(\"price\", 0)\n\n\ndef build_context(reddit_data: dict, stock_data: dict, euro: bool = False) -> str:\n    ticker_counts = reddit_data[\"ticker_counts\"]\n    top_tickers = list(ticker_counts.keys())[:TOP_N_TICKERS]\n    trends = reddit_data.get(\"trends\", {})\n\n    lines = [\n        f\"Analysis Date: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}\",\n        f\"Total Reddit Posts Analyzed: {len(reddit_data['posts'])}\",\n        \"\",\n        \"=== STOCK DATA (with MOMENTUM TREND) ===\",\n    ]\n\n    for ticker in top_tickers:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        mentions = ticker_counts.get(ticker, 0)\n        trend = trends.get(ticker, \"steady\")\n        cap_str = f\"${sd['market_cap']/1e9:.1f}B\" if sd[\"market_cap\"] else \"N/A\"\n        target_str = f\"${sd['target_mean']:.2f}\" if sd.get(\"target_mean\") else \"N/A\"\n        short_str = f\"{sd['short_pct']*100:.1f}%\" if sd.get(\"short_pct\") else \"N/A\"\n\n        lines.append(\n            f\"${ticker} ({sd['short_name']}): \"\n            f\"Price=${sd['price']:.2f}, \"\n            f\"MktCap={cap_str}, \"\n            f\"Sector={sd['sector']}, \"\n            f\"1WkChange={sd['week_change_pct']:+.2f}%, \"\n            f\"P/E={sd.get('pe_ratio', 'N/A')}, \"\n            f\"FwdP/E={sd.get('forward_pe', 'N/A')}, \"\n            f\"Beta={sd.get('beta', 'N/A')}, \"\n            f\"50dMA={sd.get('fifty_day_avg', 'N/A')}, \"\n            f\"200dMA={sd.get('two_hundred_day_avg', 'N/A')}, \"\n            f\"Recommendation={sd['recommendation']}, \"\n            f\"AnalystTarget={target_str}, \"\n            f\"ShortFloat={short_str}, \"\n            f\"VolRatio={sd['volume_ratio']}, \"\n            f\"RedditMentions={mentions}, \"\n            f\"MentionsTrend={trend} (rising=accelerating hype, falling=fading interest, new=first appearance, steady=same level)\"\n        )\n\n    trending_up = [t for t in top_tickers if trends.get(t) == \"rising\"]\n    trending_down = [t for t in top_tickers if trends.get(t) == \"falling\"]\n    if trending_up:\n        lines.append(f\"\\nRISING mentions (more hype this week vs last): {', '.join('$'+t for t in trending_up)}\")\n    if trending_down:\n        lines.append(f\"FALLING mentions (less hype this week vs last): {', '.join('$'+t for t in trending_down)}\")\n\n    lines.append(\"\")\n    lines.append(\"=== REDDIT MENTION RANKING ===\")\n    for ticker, count in list(ticker_counts.items())[:TOP_N_TICKERS]:\n        trend_icon = {\"rising\": \"↑\", \"falling\": \"↓\", \"new\": \"★\", \"steady\": \"→\", \"gone\": \"✗\"}.get(trends.get(ticker, \"\"), \"\")\n        lines.append(f\"  ${ticker}: {count} mentions {trend_icon}\")\n\n    if euro:\n        lines.append(\"\")\n        lines.append(\"=== NOTE ===\")\n        lines.append(\"Include European exchange equivalents section in your response.\")\n\n    return \"\\n\".join(lines)\n\n\nasync def run_analysis(context: str) -> str:\n    system_prompt = (\n        \"You are a senior quantitative analyst at a top hedge fund. \"\n        \"Your specialty is combining social media sentiment analysis with \"\n        \"fundamental and technical data to identify short-term trading opportunities. \"\n        \"Output your analysis in clear sections with specific reasoning. \"\n        \"Be concise and data-driven.\\n\\n\"\n        \"CRITICAL: A stock with RISING mentions + strong fundamentals is a buy signal. \"\n        \"A stock with FALLING mentions (fading hype) is a sell signal — do NOT pick these. \"\n        \"A stock with RISING mentions but weak fundamentals is a hype trap — be skeptical. \"\n        \"Weight fundamentals (P/E, beta, analyst consensus) at least as heavily as Reddit sentiment.\"\n    )\n\n    user_prompt = f\"\"\"Analyze the following data and determine which single stock has the highest probability of delivering the best return over the NEXT 7 DAYS (1 week).\n\nConsider:\n1. Reddit hype/momentum\n2. Recent price action and technical setup\n3. Valuation (P/E, forward P/E)\n4. Short squeeze potential\n5. Volume anomalies\n6. Analyst sentiment and price targets\n7. Sector momentum\n\nOutput format:\n\n**TOP PICK: $TICKER**\n- Rationale: (2-3 sentences)\n\n**RUNNER-UP: $TICKER**\n- Rationale: (1-2 sentences)\n\n**WILDCARD (High Risk/Reward): $TICKER**\n- Rationale: (1-2 sentences)\n\n**Risk Factors to Watch:**\n- (2-3 bullet points)\n\n**Confidence Score: X/10**\n- (1 sentence)\n\nDATA:\n{context}\"\"\"\n\n    loop = asyncio.get_event_loop()\n    try:\n        response = await loop.run_in_executor(\n            None,\n            lambda: client.chat.completions.create(\n                model=\"deepseek-v4-flash\",\n                messages=[\n                    {\"role\": \"system\", \"content\": system_prompt},\n                    {\"role\": \"user\", \"content\": user_prompt},\n                ],\n                temperature=0.3,\n                max_tokens=1200,\n            ),\n        )\n        return response.choices[0].message.content or \"(no response)\"\n    except Exception as e:\n        return f\"Error calling Deepseek API: {e}\"\n\n\nasync def send_event(msg_type: str, data: dict):\n    yield f\"data: {json.dumps({'type': msg_type, **data})}\\n\\n\"\n\n\nasync def event_stream(posts_per_sub: int, euro: bool):\n    try:\n        yield f\"data: {json.dumps({'type': 'status', 'message': 'Scraping Reddit...'})}\\n\\n\"\n\n        loop = asyncio.get_event_loop()\n        reddit_data = await loop.run_in_executor(None, lambda: scrape_all(limit_per_sub=posts_per_sub))\n\n        ticker_count = len(reddit_data[\"ticker_counts\"])\n        yield f\"data: {json.dumps({'type': 'status', 'message': f'Found {ticker_count} unique tickers. Fetching stock data...'})}\\n\\n\"\n\n        top_tickers = list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]\n        stock_data = await loop.run_in_executor(None, fetch_stock_data, top_tickers)\n\n        yield f\"data: {json.dumps({'type': 'status', 'message': f'Stock data ready for {len(stock_data)} tickers. Sending to AI...'})}\\n\\n\"\n\n        stock_rows = []\n        for ticker in list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]:\n            sd = stock_data.get(ticker)\n            if not sd:\n                continue\n            stock_rows.append({\n                \"ticker\": ticker,\n                \"price\": sd[\"price\"],\n                \"currency\": sd[\"currency\"],\n                \"market_cap\": sd[\"market_cap\"],\n                \"sector\": sd[\"sector\"],\n                \"short_name\": sd[\"short_name\"],\n                \"week_change_pct\": sd[\"week_change_pct\"],\n                \"pe_ratio\": sd.get(\"pe_ratio\"),\n                \"forward_pe\": sd.get(\"forward_pe\"),\n                \"beta\": sd.get(\"beta\"),\n                \"volume_ratio\": sd[\"volume_ratio\"],\n                \"short_pct\": sd.get(\"short_pct\"),\n                \"recommendation\": sd[\"recommendation\"],\n                \"target_mean\": sd.get(\"target_mean\"),\n                \"mentions\": reddit_data[\"ticker_counts\"].get(ticker, 0),\n                \"trend\": reddit_data.get(\"trends\", {}).get(ticker, \"steady\"),\n            })\n\n        context = build_context(reddit_data, stock_data, euro=euro)\n        analysis = await run_analysis(context)\n\n        picks = extract_top_picks(analysis)\n        top_ticker = picks[0] if len(picks) > 0 else \"\"\n        runner_ticker = picks[1] if len(picks) > 1 else \"\"\n        wildcard_ticker = picks[2] if len(picks) > 2 else \"\"\n\n        save_pick(\n            top_pick=top_ticker,\n            top_price=parse_top_ticker_price(top_ticker, stock_data),\n            runner_up=runner_ticker,\n            runner_up_price=parse_top_ticker_price(runner_ticker, stock_data),\n            wildcard=wildcard_ticker,\n            wildcard_price=parse_top_ticker_price(wildcard_ticker, stock_data),\n            confidence=parse_confidence(analysis),\n            posts_scraped=len(reddit_data[\"posts\"]),\n            tickers_found=len(reddit_data[\"ticker_counts\"]),\n            analysis=analysis,\n        )\n\n        euro_data = []\n        if euro:\n            picks = extract_top_picks(analysis)\n            for us_ticker in picks:\n                eq = await loop.run_in_executor(None, find_european_equivalents, us_ticker)\n                euro_data.append({\"us_ticker\": us_ticker, \"equivalents\": eq})\n\n        yield f\"data: {json.dumps({'type': 'result', 'stocks': stock_rows, 'analysis': analysis, 'euro': euro_data})}\\n\\n\"\n        yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n    except Exception as e:\n        yield f\"data: {json.dumps({'type': 'error', 'message': str(e)})}\\n\\n\"\n        yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n\n@app.get(\"/api/analyze\")\nasync def analyze(\n    posts: int = Query(25, ge=10, le=25),\n    euro: bool = Query(False),\n):\n    return StreamingResponse(\n        event_stream(posts, euro),\n        media_type=\"text/event-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"X-Accel-Buffering\": \"no\",\n        },\n    )\n\n\n@app.get(\"/\")\nasync def index():\n    return HTMLResponse(open(\"static/index.html\").read())\n\n\n@app.get(\"/app\")\nasync def app_page():\n    return HTMLResponse(open(\"static/app.html\").read())\n\n\n@app.get(\"/track-record\")\nasync def track_record_page():\n    return HTMLResponse(open(\"static/track-record.html\").read())\n\n\n@app.get(\"/api/track-record\")\nasync def track_record_api():\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(None, compute_performance)\n\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n"}, "8ecfb3655ac7b4ee3a0aaf8ce65d1e8dda50bb6e": {"t": 1785752906.0325675, "n": 10131, "h": "6950f79d4a839c21", "c": "#!/usr/bin/env python3\nimport asyncio\nimport json\nimport os\nimport re\nimport sys\nfrom datetime import datetime\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom fastapi import FastAPI, Query\nfrom fastapi.responses import HTMLResponse, StreamingResponse\nfrom fastapi.staticfiles import StaticFiles\n\nfrom reddit_scraper import scrape_all\nfrom stock_data import fetch_stock_data, find_european_equivalents\nfrom backtest import save_pick, compute_performance\n\nload_dotenv()\n\nDEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\nDEEPSEEK_BASE_URL = \"https://api.deepseek.com/v1\"\n\nclient = OpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL)\napp = FastAPI(title=\"Reddit Stonks\")\nTOP_N_TICKERS = 20\n\n\ndef extract_top_picks(analysis: str) -> list[str]:\n    picks: list[str] = []\n    for match in re.finditer(\n        r\"\\*\\*TOP PICK:\\s*\\$?(\\w+)\\*\\*|\\*\\*RUNNER-UP:\\s*\\$?(\\w+)\\*\\*|\\*\\*WILDCARD[^*]*:\\s*\\$?(\\w+)\\*\\*\",\n        analysis,\n    ):\n        ticker = match.group(1) or match.group(2) or match.group(3)\n        if ticker:\n            picks.append(ticker)\n    return picks\n\n\ndef parse_confidence(analysis: str) -> int:\n    m = re.search(r\"Confidence Score:\\s*(\\d+)/10\", analysis, re.IGNORECASE)\n    return int(m.group(1)) if m else 0\n\n\ndef parse_top_ticker_price(ticker: str, stock_data: dict) -> float:\n    sd = stock_data.get(ticker, {})\n    return sd.get(\"price\", 0)\n\n\ndef build_context(reddit_data: dict, stock_data: dict, euro: bool = False) -> str:\n    ticker_counts = reddit_data[\"ticker_counts\"]\n    top_tickers = list(ticker_counts.keys())[:TOP_N_TICKERS]\n    trends = reddit_data.get(\"trends\", {})\n\n    lines = [\n        f\"Analysis Date: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}\",\n        f\"Total Reddit Posts Analyzed: {len(reddit_data['posts'])}\",\n        \"\",\n        \"=== STOCK DATA (with MOMENTUM TREND) ===\",\n    ]\n\n    for ticker in top_tickers:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        mentions = ticker_counts.get(ticker, 0)\n        trend = trends.get(ticker, \"steady\")\n        cap_str = f\"${sd['market_cap']/1e9:.1f}B\" if sd[\"market_cap\"] else \"N/A\"\n        target_str = f\"${sd['target_mean']:.2f}\" if sd.get(\"target_mean\") else \"N/A\"\n        short_str = f\"{sd['short_pct']*100:.1f}%\" if sd.get(\"short_pct\") else \"N/A\"\n\n        lines.append(\n            f\"${ticker} ({sd['short_name']}): \"\n            f\"Price=${sd['price']:.2f}, \"\n            f\"MktCap={cap_str}, \"\n            f\"Sector={sd['sector']}, \"\n            f\"1WkChange={sd['week_change_pct']:+.2f}%, \"\n            f\"P/E={sd.get('pe_ratio', 'N/A')}, \"\n            f\"FwdP/E={sd.get('forward_pe', 'N/A')}, \"\n            f\"Beta={sd.get('beta', 'N/A')}, \"\n            f\"50dMA={sd.get('fifty_day_avg', 'N/A')}, \"\n            f\"200dMA={sd.get('two_hundred_day_avg', 'N/A')}, \"\n            f\"Recommendation={sd['recommendation']}, \"\n            f\"AnalystTarget={target_str}, \"\n            f\"ShortFloat={short_str}, \"\n            f\"VolRatio={sd['volume_ratio']}, \"\n            f\"RedditMentions={mentions}, \"\n            f\"MentionsTrend={trend} (rising=accelerating hype, falling=fading interest, new=first appearance, steady=same level)\"\n        )\n\n    trending_up = [t for t in top_tickers if trends.get(t) == \"rising\"]\n    trending_down = [t for t in top_tickers if trends.get(t) == \"falling\"]\n    if trending_up:\n        lines.append(f\"\\nRISING mentions (more hype this week vs last): {', '.join('$'+t for t in trending_up)}\")\n    if trending_down:\n        lines.append(f\"FALLING mentions (less hype this week vs last): {', '.join('$'+t for t in trending_down)}\")\n\n    lines.append(\"\")\n    lines.append(\"=== REDDIT MENTION RANKING ===\")\n    for ticker, count in list(ticker_counts.items())[:TOP_N_TICKERS]:\n        trend_icon = {\"rising\": \"↑\", \"falling\": \"↓\", \"new\": \"★\", \"steady\": \"→\", \"gone\": \"✗\"}.get(trends.get(ticker, \"\"), \"\")\n        lines.append(f\"  ${ticker}: {count} mentions {trend_icon}\")\n\n    if euro:\n        lines.append(\"\")\n        lines.append(\"=== NOTE ===\")\n        lines.append(\"Include European exchange equivalents section in your response.\")\n\n    return \"\\n\".join(lines)\n\n\nasync def run_analysis(context: str) -> str:\n    system_prompt = (\n        \"You are a senior quantitative analyst at a top hedge fund. \"\n        \"Your specialty is combining social media sentiment analysis with \"\n        \"fundamental and technical data to identify short-term trading opportunities. \"\n        \"Output your analysis in clear sections with specific reasoning. \"\n        \"Be concise and data-driven.\\n\\n\"\n        \"CRITICAL: A stock with RISING mentions + strong fundamentals is a buy signal. \"\n        \"A stock with FALLING mentions (fading hype) is a sell signal — do NOT pick these. \"\n        \"A stock with RISING mentions but weak fundamentals is a hype trap — be skeptical. \"\n        \"Weight fundamentals (P/E, beta, analyst consensus) at least as heavily as Reddit sentiment.\"\n    )\n\n    user_prompt = f\"\"\"Analyze the following data and determine which single stock has the highest probability of delivering the best return over the NEXT 7 DAYS (1 week).\n\nConsider:\n1. Reddit hype/momentum\n2. Recent price action and technical setup\n3. Valuation (P/E, forward P/E)\n4. Short squeeze potential\n5. Volume anomalies\n6. Analyst sentiment and price targets\n7. Sector momentum\n\nOutput format:\n\n**TOP PICK: $TICKER**\n- Rationale: (2-3 sentences)\n\n**RUNNER-UP: $TICKER**\n- Rationale: (1-2 sentences)\n\n**WILDCARD (High Risk/Reward): $TICKER**\n- Rationale: (1-2 sentences)\n\n**Risk Factors to Watch:**\n- (2-3 bullet points)\n\n**Confidence Score: X/10**\n- (1 sentence)\n\nDATA:\n{context}\"\"\"\n\n    loop = asyncio.get_event_loop()\n    try:\n        response = await loop.run_in_executor(\n            None,\n            lambda: client.chat.completions.create(\n                model=\"deepseek-v4-flash\",\n                messages=[\n                    {\"role\": \"system\", \"content\": system_prompt},\n                    {\"role\": \"user\", \"content\": user_prompt},\n                ],\n                temperature=0.3,\n                max_tokens=1200,\n            ),\n        )\n        return response.choices[0].message.content or \"(no response)\"\n    except Exception as e:\n        return f\"Error calling Deepseek API: {e}\"\n\n\nasync def event_stream(posts_per_sub: int, euro: bool):\n    yield f\"data: {json.dumps({'type': 'status', 'message': 'Scraping Reddit...'})}\\n\\n\"\n\n    loop = asyncio.get_event_loop()\n    reddit_data = await loop.run_in_executor(None, lambda: scrape_all(limit_per_sub=posts_per_sub))\n\n    ticker_count = len(reddit_data[\"ticker_counts\"])\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Found {ticker_count} unique tickers. Fetching stock data...'})}\\n\\n\"\n\n    top_tickers = list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]\n    stock_data = await loop.run_in_executor(None, fetch_stock_data, top_tickers)\n\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Stock data ready for {len(stock_data)} tickers. Sending to AI...'})}\\n\\n\"\n\n    stock_rows = []\n    for ticker in list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        stock_rows.append({\n            \"ticker\": ticker,\n            \"price\": sd[\"price\"],\n            \"currency\": sd[\"currency\"],\n            \"market_cap\": sd[\"market_cap\"],\n            \"sector\": sd[\"sector\"],\n            \"short_name\": sd[\"short_name\"],\n            \"week_change_pct\": sd[\"week_change_pct\"],\n            \"pe_ratio\": sd.get(\"pe_ratio\"),\n            \"forward_pe\": sd.get(\"forward_pe\"),\n            \"beta\": sd.get(\"beta\"),\n            \"volume_ratio\": sd[\"volume_ratio\"],\n            \"short_pct\": sd.get(\"short_pct\"),\n            \"recommendation\": sd[\"recommendation\"],\n            \"target_mean\": sd.get(\"target_mean\"),\n            \"mentions\": reddit_data[\"ticker_counts\"].get(ticker, 0),\n            \"trend\": reddit_data.get(\"trends\", {}).get(ticker, \"steady\"),\n        })\n\n    context = build_context(reddit_data, stock_data, euro=euro)\n    analysis = await run_analysis(context)\n\n    picks = extract_top_picks(analysis)\n    top_ticker = picks[0] if len(picks) > 0 else \"\"\n    runner_ticker = picks[1] if len(picks) > 1 else \"\"\n    wildcard_ticker = picks[2] if len(picks) > 2 else \"\"\n\n    save_pick(\n        top_pick=top_ticker,\n        top_price=parse_top_ticker_price(top_ticker, stock_data),\n        runner_up=runner_ticker,\n        runner_up_price=parse_top_ticker_price(runner_ticker, stock_data),\n        wildcard=wildcard_ticker,\n        wildcard_price=parse_top_ticker_price(wildcard_ticker, stock_data),\n        confidence=parse_confidence(analysis),\n        posts_scraped=len(reddit_data[\"posts\"]),\n        tickers_found=len(reddit_data[\"ticker_counts\"]),\n        analysis=analysis,\n    )\n\n    euro_data = []\n    if euro:\n        picks = extract_top_picks(analysis)\n        for us_ticker in picks:\n            eq = await loop.run_in_executor(None, find_european_equivalents, us_ticker)\n            euro_data.append({\"us_ticker\": us_ticker, \"equivalents\": eq})\n\n    yield f\"data: {json.dumps({'type': 'result', 'stocks': stock_rows, 'analysis': analysis, 'euro': euro_data})}\\n\\n\"\n    yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n\n@app.get(\"/api/analyze\")\nasync def analyze(\n    posts: int = Query(25, ge=10, le=25),\n    euro: bool = Query(False),\n):\n    return StreamingResponse(\n        event_stream(posts, euro),\n        media_type=\"text/event-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"X-Accel-Buffering\": \"no\",\n        },\n    )\n\n\n@app.get(\"/\")\nasync def index():\n    return HTMLResponse(open(\"static/index.html\").read())\n\n\n@app.get(\"/app\")\nasync def app_page():\n    return HTMLResponse(open(\"static/app.html\").read())\n\n\n@app.get(\"/track-record\")\nasync def track_record_page():\n    return HTMLResponse(open(\"static/track-record.html\").read())\n\n\n@app.get(\"/api/track-record\")\nasync def track_record_api():\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(None, compute_performance)\n\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n"}, "0bdc2cae393221e85a62ba359cd05a27d58ed3af": {"t": 1785752907.2961493, "n": 8877, "h": "2b839d2a0bb67986", "c": "#!/usr/bin/env python3\nimport asyncio\nimport json\nimport os\nimport re\nimport sys\nfrom datetime import datetime\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom fastapi import FastAPI, Query\nfrom fastapi.responses import HTMLResponse, StreamingResponse\nfrom fastapi.staticfiles import StaticFiles\n\nfrom reddit_scraper import scrape_all\nfrom stock_data import fetch_stock_data, find_european_equivalents\nfrom backtest import save_pick, compute_performance\n\nload_dotenv()\n\nDEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\nDEEPSEEK_BASE_URL = \"https://api.deepseek.com/v1\"\n\nclient = OpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL)\napp = FastAPI(title=\"Reddit Stonks\")\nTOP_N_TICKERS = 20\n\n\ndef extract_top_picks(analysis: str) -> list[str]:\n    picks: list[str] = []\n    for match in re.finditer(\n        r\"\\*\\*TOP PICK:\\s*\\$?(\\w+)\\*\\*|\\*\\*RUNNER-UP:\\s*\\$?(\\w+)\\*\\*|\\*\\*WILDCARD[^*]*:\\s*\\$?(\\w+)\\*\\*\",\n        analysis,\n    ):\n        ticker = match.group(1) or match.group(2) or match.group(3)\n        if ticker:\n            picks.append(ticker)\n    return picks\n\n\ndef parse_confidence(analysis: str) -> int:\n    m = re.search(r\"Confidence Score:\\s*(\\d+)/10\", analysis, re.IGNORECASE)\n    return int(m.group(1)) if m else 0\n\n\ndef parse_top_ticker_price(ticker: str, stock_data: dict) -> float:\n    sd = stock_data.get(ticker, {})\n    return sd.get(\"price\", 0)\n\n\ndef build_context(reddit_data: dict, stock_data: dict, euro: bool = False) -> str:\n    ticker_counts = reddit_data[\"ticker_counts\"]\n    top_tickers = list(ticker_counts.keys())[:TOP_N_TICKERS]\n\n    lines = [\n        f\"Analysis Date: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}\",\n        f\"Total Reddit Posts Analyzed: {len(reddit_data['posts'])}\",\n        \"\",\n        \"=== STOCK DATA ===\",\n    ]\n\n    for ticker in top_tickers:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        mentions = ticker_counts.get(ticker, 0)\n        cap_str = f\"${sd['market_cap']/1e9:.1f}B\" if sd[\"market_cap\"] else \"N/A\"\n        target_str = f\"${sd['target_mean']:.2f}\" if sd.get(\"target_mean\") else \"N/A\"\n        short_str = f\"{sd['short_pct']*100:.1f}%\" if sd.get(\"short_pct\") else \"N/A\"\n\n        lines.append(\n            f\"${ticker} ({sd['short_name']}): \"\n            f\"Price=${sd['price']:.2f}, \"\n            f\"MktCap={cap_str}, \"\n            f\"Sector={sd['sector']}, \"\n            f\"1WkChange={sd['week_change_pct']:+.2f}%, \"\n            f\"P/E={sd.get('pe_ratio', 'N/A')}, \"\n            f\"FwdP/E={sd.get('forward_pe', 'N/A')}, \"\n            f\"Beta={sd.get('beta', 'N/A')}, \"\n            f\"50dMA={sd.get('fifty_day_avg', 'N/A')}, \"\n            f\"200dMA={sd.get('two_hundred_day_avg', 'N/A')}, \"\n            f\"Recommendation={sd['recommendation']}, \"\n            f\"AnalystTarget={target_str}, \"\n            f\"ShortFloat={short_str}, \"\n            f\"VolRatio={sd['volume_ratio']}, \"\n            f\"RedditMentions={mentions}\"\n        )\n\n    lines.append(\"\")\n    lines.append(\"=== REDDIT MENTION RANKING ===\")\n    for ticker, count in list(ticker_counts.items())[:TOP_N_TICKERS]:\n        lines.append(f\"  ${ticker}: {count} mentions\")\n\n    if euro:\n        lines.append(\"\")\n        lines.append(\"=== NOTE ===\")\n        lines.append(\"Include European exchange equivalents section in your response.\")\n\n    return \"\\n\".join(lines)\n\n\nasync def run_analysis(context: str) -> str:\n    system_prompt = (\n        \"You are a senior quantitative analyst at a top hedge fund. \"\n        \"Your specialty is combining social media sentiment analysis with \"\n        \"fundamental and technical data to identify short-term trading opportunities. \"\n        \"Output your analysis in clear sections with specific reasoning. \"\n        \"Be concise and data-driven.\"\n    )\n\n    user_prompt = f\"\"\"Analyze the following data and determine which single stock has the highest probability of delivering the best return over the NEXT 7 DAYS (1 week).\n\nConsider:\n1. Reddit hype/momentum\n2. Recent price action and technical setup\n3. Valuation (P/E, forward P/E)\n4. Short squeeze potential\n5. Volume anomalies\n6. Analyst sentiment and price targets\n7. Sector momentum\n\nOutput format:\n\n**TOP PICK: $TICKER**\n- Rationale: (2-3 sentences)\n\n**RUNNER-UP: $TICKER**\n- Rationale: (1-2 sentences)\n\n**WILDCARD (High Risk/Reward): $TICKER**\n- Rationale: (1-2 sentences)\n\n**Risk Factors to Watch:**\n- (2-3 bullet points)\n\n**Confidence Score: X/10**\n- (1 sentence)\n\nDATA:\n{context}\"\"\"\n\n    loop = asyncio.get_event_loop()\n    try:\n        response = await loop.run_in_executor(\n            None,\n            lambda: client.chat.completions.create(\n                model=\"deepseek-v4-flash\",\n                messages=[\n                    {\"role\": \"system\", \"content\": system_prompt},\n                    {\"role\": \"user\", \"content\": user_prompt},\n                ],\n                temperature=0.3,\n                max_tokens=1200,\n            ),\n        )\n        return response.choices[0].message.content or \"(no response)\"\n    except Exception as e:\n        return f\"Error calling Deepseek API: {e}\"\n\n\nasync def event_stream(posts_per_sub: int, euro: bool):\n    yield f\"data: {json.dumps({'type': 'status', 'message': 'Scraping Reddit...'})}\\n\\n\"\n\n    loop = asyncio.get_event_loop()\n    reddit_data = await loop.run_in_executor(None, lambda: scrape_all(limit_per_sub=posts_per_sub))\n\n    ticker_count = len(reddit_data[\"ticker_counts\"])\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Found {ticker_count} unique tickers. Fetching stock data...'})}\\n\\n\"\n\n    top_tickers = list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]\n    stock_data = await loop.run_in_executor(None, fetch_stock_data, top_tickers)\n\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Stock data ready for {len(stock_data)} tickers. Sending to AI...'})}\\n\\n\"\n\n    stock_rows = []\n    for ticker in list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        stock_rows.append({\n            \"ticker\": ticker,\n            \"price\": sd[\"price\"],\n            \"currency\": sd[\"currency\"],\n            \"market_cap\": sd[\"market_cap\"],\n            \"sector\": sd[\"sector\"],\n            \"short_name\": sd[\"short_name\"],\n            \"week_change_pct\": sd[\"week_change_pct\"],\n            \"pe_ratio\": sd.get(\"pe_ratio\"),\n            \"forward_pe\": sd.get(\"forward_pe\"),\n            \"beta\": sd.get(\"beta\"),\n            \"volume_ratio\": sd[\"volume_ratio\"],\n            \"short_pct\": sd.get(\"short_pct\"),\n            \"recommendation\": sd[\"recommendation\"],\n            \"target_mean\": sd.get(\"target_mean\"),\n            \"mentions\": reddit_data[\"ticker_counts\"].get(ticker, 0),\n        })\n\n    context = build_context(reddit_data, stock_data, euro=euro)\n    analysis = await run_analysis(context)\n\n    picks = extract_top_picks(analysis)\n    top_ticker = picks[0] if len(picks) > 0 else \"\"\n    runner_ticker = picks[1] if len(picks) > 1 else \"\"\n    wildcard_ticker = picks[2] if len(picks) > 2 else \"\"\n\n    save_pick(\n        top_pick=top_ticker,\n        top_price=parse_top_ticker_price(top_ticker, stock_data),\n        runner_up=runner_ticker,\n        runner_up_price=parse_top_ticker_price(runner_ticker, stock_data),\n        wildcard=wildcard_ticker,\n        wildcard_price=parse_top_ticker_price(wildcard_ticker, stock_data),\n        confidence=parse_confidence(analysis),\n        posts_scraped=len(reddit_data[\"posts\"]),\n        tickers_found=len(reddit_data[\"ticker_counts\"]),\n        analysis=analysis,\n    )\n\n    euro_data = []\n    if euro:\n        picks = extract_top_picks(analysis)\n        for us_ticker in picks:\n            eq = await loop.run_in_executor(None, find_european_equivalents, us_ticker)\n            euro_data.append({\"us_ticker\": us_ticker, \"equivalents\": eq})\n\n    yield f\"data: {json.dumps({'type': 'result', 'stocks': stock_rows, 'analysis': analysis, 'euro': euro_data})}\\n\\n\"\n    yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n\n@app.get(\"/api/analyze\")\nasync def analyze(\n    posts: int = Query(25, ge=10, le=25),\n    euro: bool = Query(False),\n):\n    return StreamingResponse(\n        event_stream(posts, euro),\n        media_type=\"text/event-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"X-Accel-Buffering\": \"no\",\n        },\n    )\n\n\n@app.get(\"/\")\nasync def index():\n    return HTMLResponse(open(\"static/index.html\").read())\n\n\n@app.get(\"/app\")\nasync def app_page():\n    return HTMLResponse(open(\"static/app.html\").read())\n\n\n@app.get(\"/track-record\")\nasync def track_record_page():\n    return HTMLResponse(open(\"static/track-record.html\").read())\n\n\n@app.get(\"/api/track-record\")\nasync def track_record_api():\n    loop = asyncio.get_event_loop()\n    return await loop.run_in_executor(None, compute_performance)\n\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n"}, "71136da4fd60da53fe38c64e72765079e5fb670c": {"t": 1785752908.5688648, "n": 7521, "h": "01b3699020e2185a", "c": "#!/usr/bin/env python3\nimport asyncio\nimport json\nimport os\nimport re\nimport sys\nfrom datetime import datetime\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom fastapi import FastAPI, Query\nfrom fastapi.responses import HTMLResponse, StreamingResponse\nfrom fastapi.staticfiles import StaticFiles\n\nfrom reddit_scraper import scrape_all\nfrom stock_data import fetch_stock_data, find_european_equivalents\n\nload_dotenv()\n\nDEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\nDEEPSEEK_BASE_URL = \"https://api.deepseek.com/v1\"\n\nclient = OpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL)\napp = FastAPI(title=\"Reddit Stonks\")\nTOP_N_TICKERS = 20\n\n\ndef extract_top_picks(analysis: str) -> list[str]:\n    picks: list[str] = []\n    for match in re.finditer(\n        r\"\\*\\*TOP PICK:\\s*\\$?(\\w+)\\*\\*|\\*\\*RUNNER-UP:\\s*\\$?(\\w+)\\*\\*|\\*\\*WILDCARD[^*]*:\\s*\\$?(\\w+)\\*\\*\",\n        analysis,\n    ):\n        ticker = match.group(1) or match.group(2) or match.group(3)\n        if ticker:\n            picks.append(ticker)\n    return picks\n\n\ndef build_context(reddit_data: dict, stock_data: dict, euro: bool = False) -> str:\n    ticker_counts = reddit_data[\"ticker_counts\"]\n    top_tickers = list(ticker_counts.keys())[:TOP_N_TICKERS]\n\n    lines = [\n        f\"Analysis Date: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}\",\n        f\"Total Reddit Posts Analyzed: {len(reddit_data['posts'])}\",\n        \"\",\n        \"=== STOCK DATA ===\",\n    ]\n\n    for ticker in top_tickers:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        mentions = ticker_counts.get(ticker, 0)\n        cap_str = f\"${sd['market_cap']/1e9:.1f}B\" if sd[\"market_cap\"] else \"N/A\"\n        target_str = f\"${sd['target_mean']:.2f}\" if sd.get(\"target_mean\") else \"N/A\"\n        short_str = f\"{sd['short_pct']*100:.1f}%\" if sd.get(\"short_pct\") else \"N/A\"\n\n        lines.append(\n            f\"${ticker} ({sd['short_name']}): \"\n            f\"Price=${sd['price']:.2f}, \"\n            f\"MktCap={cap_str}, \"\n            f\"Sector={sd['sector']}, \"\n            f\"1WkChange={sd['week_change_pct']:+.2f}%, \"\n            f\"P/E={sd.get('pe_ratio', 'N/A')}, \"\n            f\"FwdP/E={sd.get('forward_pe', 'N/A')}, \"\n            f\"Beta={sd.get('beta', 'N/A')}, \"\n            f\"50dMA={sd.get('fifty_day_avg', 'N/A')}, \"\n            f\"200dMA={sd.get('two_hundred_day_avg', 'N/A')}, \"\n            f\"Recommendation={sd['recommendation']}, \"\n            f\"AnalystTarget={target_str}, \"\n            f\"ShortFloat={short_str}, \"\n            f\"VolRatio={sd['volume_ratio']}, \"\n            f\"RedditMentions={mentions}\"\n        )\n\n    lines.append(\"\")\n    lines.append(\"=== REDDIT MENTION RANKING ===\")\n    for ticker, count in list(ticker_counts.items())[:TOP_N_TICKERS]:\n        lines.append(f\"  ${ticker}: {count} mentions\")\n\n    if euro:\n        lines.append(\"\")\n        lines.append(\"=== NOTE ===\")\n        lines.append(\"Include European exchange equivalents section in your response.\")\n\n    return \"\\n\".join(lines)\n\n\nasync def run_analysis(context: str) -> str:\n    system_prompt = (\n        \"You are a senior quantitative analyst at a top hedge fund. \"\n        \"Your specialty is combining social media sentiment analysis with \"\n        \"fundamental and technical data to identify short-term trading opportunities. \"\n        \"Output your analysis in clear sections with specific reasoning. \"\n        \"Be concise and data-driven.\"\n    )\n\n    user_prompt = f\"\"\"Analyze the following data and determine which single stock has the highest probability of delivering the best return over the NEXT 7 DAYS (1 week).\n\nConsider:\n1. Reddit hype/momentum\n2. Recent price action and technical setup\n3. Valuation (P/E, forward P/E)\n4. Short squeeze potential\n5. Volume anomalies\n6. Analyst sentiment and price targets\n7. Sector momentum\n\nOutput format:\n\n**TOP PICK: $TICKER**\n- Rationale: (2-3 sentences)\n\n**RUNNER-UP: $TICKER**\n- Rationale: (1-2 sentences)\n\n**WILDCARD (High Risk/Reward): $TICKER**\n- Rationale: (1-2 sentences)\n\n**Risk Factors to Watch:**\n- (2-3 bullet points)\n\n**Confidence Score: X/10**\n- (1 sentence)\n\nDATA:\n{context}\"\"\"\n\n    loop = asyncio.get_event_loop()\n    try:\n        response = await loop.run_in_executor(\n            None,\n            lambda: client.chat.completions.create(\n                model=\"deepseek-v4-flash\",\n                messages=[\n                    {\"role\": \"system\", \"content\": system_prompt},\n                    {\"role\": \"user\", \"content\": user_prompt},\n                ],\n                temperature=0.3,\n                max_tokens=1200,\n            ),\n        )\n        return response.choices[0].message.content or \"(no response)\"\n    except Exception as e:\n        return f\"Error calling Deepseek API: {e}\"\n\n\nasync def event_stream(posts_per_sub: int, euro: bool):\n    yield f\"data: {json.dumps({'type': 'status', 'message': 'Scraping Reddit...'})}\\n\\n\"\n\n    loop = asyncio.get_event_loop()\n    reddit_data = await loop.run_in_executor(None, lambda: scrape_all(limit_per_sub=posts_per_sub))\n\n    ticker_count = len(reddit_data[\"ticker_counts\"])\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Found {ticker_count} unique tickers. Fetching stock data...'})}\\n\\n\"\n\n    top_tickers = list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]\n    stock_data = await loop.run_in_executor(None, fetch_stock_data, top_tickers)\n\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Stock data ready for {len(stock_data)} tickers. Sending to AI...'})}\\n\\n\"\n\n    stock_rows = []\n    for ticker in list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        stock_rows.append({\n            \"ticker\": ticker,\n            \"price\": sd[\"price\"],\n            \"currency\": sd[\"currency\"],\n            \"market_cap\": sd[\"market_cap\"],\n            \"sector\": sd[\"sector\"],\n            \"short_name\": sd[\"short_name\"],\n            \"week_change_pct\": sd[\"week_change_pct\"],\n            \"pe_ratio\": sd.get(\"pe_ratio\"),\n            \"forward_pe\": sd.get(\"forward_pe\"),\n            \"beta\": sd.get(\"beta\"),\n            \"volume_ratio\": sd[\"volume_ratio\"],\n            \"short_pct\": sd.get(\"short_pct\"),\n            \"recommendation\": sd[\"recommendation\"],\n            \"target_mean\": sd.get(\"target_mean\"),\n            \"mentions\": reddit_data[\"ticker_counts\"].get(ticker, 0),\n        })\n\n    context = build_context(reddit_data, stock_data, euro=euro)\n    analysis = await run_analysis(context)\n\n    euro_data = []\n    if euro:\n        picks = extract_top_picks(analysis)\n        for us_ticker in picks:\n            eq = await loop.run_in_executor(None, find_european_equivalents, us_ticker)\n            euro_data.append({\"us_ticker\": us_ticker, \"equivalents\": eq})\n\n    yield f\"data: {json.dumps({'type': 'result', 'stocks': stock_rows, 'analysis': analysis, 'euro': euro_data})}\\n\\n\"\n    yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n\n@app.get(\"/api/analyze\")\nasync def analyze(\n    posts: int = Query(25, ge=5, le=100),\n    euro: bool = Query(False),\n):\n    return StreamingResponse(\n        event_stream(posts, euro),\n        media_type=\"text/event-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"X-Accel-Buffering\": \"no\",\n        },\n    )\n\n\n@app.get(\"/\")\nasync def index():\n    return HTMLResponse(open(\"static/index.html\").read())\n\n\n@app.get(\"/app\")\nasync def app_page():\n    return HTMLResponse(open(\"static/app.html\").read())\n\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n"}, "d800fbee4541e2413fdcffa39a33b86e14df1ef3": {"t": 1785752910.7957227, "n": 7517, "h": "ab3d4feebf2e008b", "c": "#!/usr/bin/env python3\nimport asyncio\nimport json\nimport os\nimport re\nimport sys\nfrom datetime import datetime\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom fastapi import FastAPI, Query\nfrom fastapi.responses import HTMLResponse, StreamingResponse\nfrom fastapi.staticfiles import StaticFiles\n\nfrom reddit_scraper import scrape_all\nfrom stock_data import fetch_stock_data, find_european_equivalents\n\nload_dotenv()\n\nDEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\nDEEPSEEK_BASE_URL = \"https://api.deepseek.com/v1\"\n\nclient = OpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL)\napp = FastAPI(title=\"Reddit Stonks\")\nTOP_N_TICKERS = 20\n\n\ndef extract_top_picks(analysis: str) -> list[str]:\n    picks: list[str] = []\n    for match in re.finditer(\n        r\"\\*\\*TOP PICK:\\s*\\$?(\\w+)\\*\\*|\\*\\*RUNNER-UP:\\s*\\$?(\\w+)\\*\\*|\\*\\*WILDCARD[^*]*:\\s*\\$?(\\w+)\\*\\*\",\n        analysis,\n    ):\n        ticker = match.group(1) or match.group(2) or match.group(3)\n        if ticker:\n            picks.append(ticker)\n    return picks\n\n\ndef build_context(reddit_data: dict, stock_data: dict, euro: bool = False) -> str:\n    ticker_counts = reddit_data[\"ticker_counts\"]\n    top_tickers = list(ticker_counts.keys())[:TOP_N_TICKERS]\n\n    lines = [\n        f\"Analysis Date: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}\",\n        f\"Total Reddit Posts Analyzed: {len(reddit_data['posts'])}\",\n        \"\",\n        \"=== STOCK DATA ===\",\n    ]\n\n    for ticker in top_tickers:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        mentions = ticker_counts.get(ticker, 0)\n        cap_str = f\"${sd['market_cap']/1e9:.1f}B\" if sd[\"market_cap\"] else \"N/A\"\n        target_str = f\"${sd['target_mean']:.2f}\" if sd.get(\"target_mean\") else \"N/A\"\n        short_str = f\"{sd['short_pct']*100:.1f}%\" if sd.get(\"short_pct\") else \"N/A\"\n\n        lines.append(\n            f\"${ticker} ({sd['short_name']}): \"\n            f\"Price=${sd['price']:.2f}, \"\n            f\"MktCap={cap_str}, \"\n            f\"Sector={sd['sector']}, \"\n            f\"1WkChange={sd['week_change_pct']:+.2f}%, \"\n            f\"P/E={sd.get('pe_ratio', 'N/A')}, \"\n            f\"FwdP/E={sd.get('forward_pe', 'N/A')}, \"\n            f\"Beta={sd.get('beta', 'N/A')}, \"\n            f\"50dMA={sd.get('fifty_day_avg', 'N/A')}, \"\n            f\"200dMA={sd.get('two_hundred_day_avg', 'N/A')}, \"\n            f\"Recommendation={sd['recommendation']}, \"\n            f\"AnalystTarget={target_str}, \"\n            f\"ShortFloat={short_str}, \"\n            f\"VolRatio={sd['volume_ratio']}, \"\n            f\"RedditMentions={mentions}\"\n        )\n\n    lines.append(\"\")\n    lines.append(\"=== REDDIT MENTION RANKING ===\")\n    for ticker, count in list(ticker_counts.items())[:TOP_N_TICKERS]:\n        lines.append(f\"  ${ticker}: {count} mentions\")\n\n    if euro:\n        lines.append(\"\")\n        lines.append(\"=== NOTE ===\")\n        lines.append(\"Include European exchange equivalents section in your response.\")\n\n    return \"\\n\".join(lines)\n\n\nasync def run_analysis(context: str) -> str:\n    system_prompt = (\n        \"You are a senior quantitative analyst at a top hedge fund. \"\n        \"Your specialty is combining social media sentiment analysis with \"\n        \"fundamental and technical data to identify short-term trading opportunities. \"\n        \"Output your analysis in clear sections with specific reasoning. \"\n        \"Be concise and data-driven.\"\n    )\n\n    user_prompt = f\"\"\"Analyze the following data and determine which single stock has the highest probability of delivering the best return over the NEXT 7 DAYS (1 week).\n\nConsider:\n1. Reddit hype/momentum\n2. Recent price action and technical setup\n3. Valuation (P/E, forward P/E)\n4. Short squeeze potential\n5. Volume anomalies\n6. Analyst sentiment and price targets\n7. Sector momentum\n\nOutput format:\n\n**TOP PICK: $TICKER**\n- Rationale: (2-3 sentences)\n\n**RUNNER-UP: $TICKER**\n- Rationale: (1-2 sentences)\n\n**WILDCARD (High Risk/Reward): $TICKER**\n- Rationale: (1-2 sentences)\n\n**Risk Factors to Watch:**\n- (2-3 bullet points)\n\n**Confidence Score: X/10**\n- (1 sentence)\n\nDATA:\n{context}\"\"\"\n\n    loop = asyncio.get_event_loop()\n    try:\n        response = await loop.run_in_executor(\n            None,\n            lambda: client.chat.completions.create(\n                model=\"deepseek-chat\",\n                messages=[\n                    {\"role\": \"system\", \"content\": system_prompt},\n                    {\"role\": \"user\", \"content\": user_prompt},\n                ],\n                temperature=0.3,\n                max_tokens=1200,\n            ),\n        )\n        return response.choices[0].message.content or \"(no response)\"\n    except Exception as e:\n        return f\"Error calling Deepseek API: {e}\"\n\n\nasync def event_stream(posts_per_sub: int, euro: bool):\n    yield f\"data: {json.dumps({'type': 'status', 'message': 'Scraping Reddit...'})}\\n\\n\"\n\n    loop = asyncio.get_event_loop()\n    reddit_data = await loop.run_in_executor(None, lambda: scrape_all(limit_per_sub=posts_per_sub))\n\n    ticker_count = len(reddit_data[\"ticker_counts\"])\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Found {ticker_count} unique tickers. Fetching stock data...'})}\\n\\n\"\n\n    top_tickers = list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]\n    stock_data = await loop.run_in_executor(None, fetch_stock_data, top_tickers)\n\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Stock data ready for {len(stock_data)} tickers. Sending to AI...'})}\\n\\n\"\n\n    stock_rows = []\n    for ticker in list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        stock_rows.append({\n            \"ticker\": ticker,\n            \"price\": sd[\"price\"],\n            \"currency\": sd[\"currency\"],\n            \"market_cap\": sd[\"market_cap\"],\n            \"sector\": sd[\"sector\"],\n            \"short_name\": sd[\"short_name\"],\n            \"week_change_pct\": sd[\"week_change_pct\"],\n            \"pe_ratio\": sd.get(\"pe_ratio\"),\n            \"forward_pe\": sd.get(\"forward_pe\"),\n            \"beta\": sd.get(\"beta\"),\n            \"volume_ratio\": sd[\"volume_ratio\"],\n            \"short_pct\": sd.get(\"short_pct\"),\n            \"recommendation\": sd[\"recommendation\"],\n            \"target_mean\": sd.get(\"target_mean\"),\n            \"mentions\": reddit_data[\"ticker_counts\"].get(ticker, 0),\n        })\n\n    context = build_context(reddit_data, stock_data, euro=euro)\n    analysis = await run_analysis(context)\n\n    euro_data = []\n    if euro:\n        picks = extract_top_picks(analysis)\n        for us_ticker in picks:\n            eq = await loop.run_in_executor(None, find_european_equivalents, us_ticker)\n            euro_data.append({\"us_ticker\": us_ticker, \"equivalents\": eq})\n\n    yield f\"data: {json.dumps({'type': 'result', 'stocks': stock_rows, 'analysis': analysis, 'euro': euro_data})}\\n\\n\"\n    yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n\n@app.get(\"/api/analyze\")\nasync def analyze(\n    posts: int = Query(25, ge=5, le=100),\n    euro: bool = Query(False),\n):\n    return StreamingResponse(\n        event_stream(posts, euro),\n        media_type=\"text/event-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"X-Accel-Buffering\": \"no\",\n        },\n    )\n\n\n@app.get(\"/\")\nasync def index():\n    return HTMLResponse(open(\"static/index.html\").read())\n\n\n@app.get(\"/app\")\nasync def app_page():\n    return HTMLResponse(open(\"static/app.html\").read())\n\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n"}, "7e2f2d661a88153984dcd4d2014ec5275d4cbe4e": {"t": 1785752912.2557886, "n": 7420, "h": "136b3173637a2357", "c": "#!/usr/bin/env python3\nimport asyncio\nimport json\nimport os\nimport re\nimport sys\nfrom datetime import datetime\n\nfrom dotenv import load_dotenv\nfrom openai import OpenAI\nfrom fastapi import FastAPI, Query\nfrom fastapi.responses import HTMLResponse, StreamingResponse\nfrom fastapi.staticfiles import StaticFiles\n\nfrom reddit_scraper import scrape_all\nfrom stock_data import fetch_stock_data, find_european_equivalents\n\nload_dotenv()\n\nDEEPSEEK_API_KEY = os.getenv(\"DEEPSEEK_API_KEY\")\nDEEPSEEK_BASE_URL = \"https://api.deepseek.com/v1\"\n\nclient = OpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL)\napp = FastAPI(title=\"Reddit Stonks\")\nTOP_N_TICKERS = 20\n\n\ndef extract_top_picks(analysis: str) -> list[str]:\n    picks: list[str] = []\n    for match in re.finditer(\n        r\"\\*\\*TOP PICK:\\s*\\$?(\\w+)\\*\\*|\\*\\*RUNNER-UP:\\s*\\$?(\\w+)\\*\\*|\\*\\*WILDCARD[^*]*:\\s*\\$?(\\w+)\\*\\*\",\n        analysis,\n    ):\n        ticker = match.group(1) or match.group(2) or match.group(3)\n        if ticker:\n            picks.append(ticker)\n    return picks\n\n\ndef build_context(reddit_data: dict, stock_data: dict, euro: bool = False) -> str:\n    ticker_counts = reddit_data[\"ticker_counts\"]\n    top_tickers = list(ticker_counts.keys())[:TOP_N_TICKERS]\n\n    lines = [\n        f\"Analysis Date: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}\",\n        f\"Total Reddit Posts Analyzed: {len(reddit_data['posts'])}\",\n        \"\",\n        \"=== STOCK DATA ===\",\n    ]\n\n    for ticker in top_tickers:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        mentions = ticker_counts.get(ticker, 0)\n        cap_str = f\"${sd['market_cap']/1e9:.1f}B\" if sd[\"market_cap\"] else \"N/A\"\n        target_str = f\"${sd['target_mean']:.2f}\" if sd.get(\"target_mean\") else \"N/A\"\n        short_str = f\"{sd['short_pct']*100:.1f}%\" if sd.get(\"short_pct\") else \"N/A\"\n\n        lines.append(\n            f\"${ticker} ({sd['short_name']}): \"\n            f\"Price=${sd['price']:.2f}, \"\n            f\"MktCap={cap_str}, \"\n            f\"Sector={sd['sector']}, \"\n            f\"1WkChange={sd['week_change_pct']:+.2f}%, \"\n            f\"P/E={sd.get('pe_ratio', 'N/A')}, \"\n            f\"FwdP/E={sd.get('forward_pe', 'N/A')}, \"\n            f\"Beta={sd.get('beta', 'N/A')}, \"\n            f\"50dMA={sd.get('fifty_day_avg', 'N/A')}, \"\n            f\"200dMA={sd.get('two_hundred_day_avg', 'N/A')}, \"\n            f\"Recommendation={sd['recommendation']}, \"\n            f\"AnalystTarget={target_str}, \"\n            f\"ShortFloat={short_str}, \"\n            f\"VolRatio={sd['volume_ratio']}, \"\n            f\"RedditMentions={mentions}\"\n        )\n\n    lines.append(\"\")\n    lines.append(\"=== REDDIT MENTION RANKING ===\")\n    for ticker, count in list(ticker_counts.items())[:TOP_N_TICKERS]:\n        lines.append(f\"  ${ticker}: {count} mentions\")\n\n    if euro:\n        lines.append(\"\")\n        lines.append(\"=== NOTE ===\")\n        lines.append(\"Include European exchange equivalents section in your response.\")\n\n    return \"\\n\".join(lines)\n\n\nasync def run_analysis(context: str) -> str:\n    system_prompt = (\n        \"You are a senior quantitative analyst at a top hedge fund. \"\n        \"Your specialty is combining social media sentiment analysis with \"\n        \"fundamental and technical data to identify short-term trading opportunities. \"\n        \"Output your analysis in clear sections with specific reasoning. \"\n        \"Be concise and data-driven.\"\n    )\n\n    user_prompt = f\"\"\"Analyze the following data and determine which single stock has the highest probability of delivering the best return over the NEXT 7 DAYS (1 week).\n\nConsider:\n1. Reddit hype/momentum\n2. Recent price action and technical setup\n3. Valuation (P/E, forward P/E)\n4. Short squeeze potential\n5. Volume anomalies\n6. Analyst sentiment and price targets\n7. Sector momentum\n\nOutput format:\n\n**TOP PICK: $TICKER**\n- Rationale: (2-3 sentences)\n\n**RUNNER-UP: $TICKER**\n- Rationale: (1-2 sentences)\n\n**WILDCARD (High Risk/Reward): $TICKER**\n- Rationale: (1-2 sentences)\n\n**Risk Factors to Watch:**\n- (2-3 bullet points)\n\n**Confidence Score: X/10**\n- (1 sentence)\n\nDATA:\n{context}\"\"\"\n\n    loop = asyncio.get_event_loop()\n    try:\n        response = await loop.run_in_executor(\n            None,\n            lambda: client.chat.completions.create(\n                model=\"deepseek-chat\",\n                messages=[\n                    {\"role\": \"system\", \"content\": system_prompt},\n                    {\"role\": \"user\", \"content\": user_prompt},\n                ],\n                temperature=0.3,\n                max_tokens=1200,\n            ),\n        )\n        return response.choices[0].message.content or \"(no response)\"\n    except Exception as e:\n        return f\"Error calling Deepseek API: {e}\"\n\n\nasync def event_stream(posts_per_sub: int, euro: bool):\n    yield f\"data: {json.dumps({'type': 'status', 'message': 'Scraping Reddit...'})}\\n\\n\"\n\n    loop = asyncio.get_event_loop()\n    reddit_data = await loop.run_in_executor(None, lambda: scrape_all(limit_per_sub=posts_per_sub))\n\n    ticker_count = len(reddit_data[\"ticker_counts\"])\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Found {ticker_count} unique tickers. Fetching stock data...'})}\\n\\n\"\n\n    top_tickers = list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]\n    stock_data = await loop.run_in_executor(None, fetch_stock_data, top_tickers)\n\n    yield f\"data: {json.dumps({'type': 'status', 'message': f'Stock data ready for {len(stock_data)} tickers. Sending to AI...'})}\\n\\n\"\n\n    stock_rows = []\n    for ticker in list(reddit_data[\"ticker_counts\"].keys())[:TOP_N_TICKERS]:\n        sd = stock_data.get(ticker)\n        if not sd:\n            continue\n        stock_rows.append({\n            \"ticker\": ticker,\n            \"price\": sd[\"price\"],\n            \"currency\": sd[\"currency\"],\n            \"market_cap\": sd[\"market_cap\"],\n            \"sector\": sd[\"sector\"],\n            \"short_name\": sd[\"short_name\"],\n            \"week_change_pct\": sd[\"week_change_pct\"],\n            \"pe_ratio\": sd.get(\"pe_ratio\"),\n            \"forward_pe\": sd.get(\"forward_pe\"),\n            \"beta\": sd.get(\"beta\"),\n            \"volume_ratio\": sd[\"volume_ratio\"],\n            \"short_pct\": sd.get(\"short_pct\"),\n            \"recommendation\": sd[\"recommendation\"],\n            \"target_mean\": sd.get(\"target_mean\"),\n            \"mentions\": reddit_data[\"ticker_counts\"].get(ticker, 0),\n        })\n\n    context = build_context(reddit_data, stock_data, euro=euro)\n    analysis = await run_analysis(context)\n\n    euro_data = []\n    if euro:\n        picks = extract_top_picks(analysis)\n        for us_ticker in picks:\n            eq = await loop.run_in_executor(None, find_european_equivalents, us_ticker)\n            euro_data.append({\"us_ticker\": us_ticker, \"equivalents\": eq})\n\n    yield f\"data: {json.dumps({'type': 'result', 'stocks': stock_rows, 'analysis': analysis, 'euro': euro_data})}\\n\\n\"\n    yield f\"data: {json.dumps({'type': 'done'})}\\n\\n\"\n\n\n@app.get(\"/api/analyze\")\nasync def analyze(\n    posts: int = Query(25, ge=5, le=100),\n    euro: bool = Query(False),\n):\n    return StreamingResponse(\n        event_stream(posts, euro),\n        media_type=\"text/event-stream\",\n        headers={\n            \"Cache-Control\": \"no-cache\",\n            \"X-Accel-Buffering\": \"no\",\n        },\n    )\n\n\n@app.get(\"/\")\nasync def index():\n    return HTMLResponse(open(\"static/index.html\").read())\n\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n"}}}}