{"id":2486,"date":"2026-03-13T10:00:00","date_gmt":"2026-03-13T10:00:00","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=2486"},"modified":"2026-03-12T21:36:28","modified_gmt":"2026-03-12T21:36:28","slug":"prediction-market-terminal-python-cli","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/prediction-market-terminal-python-cli\/","title":{"rendered":"Prediction Market Terminal: Build a CLI Trading Monitor with Python"},"content":{"rendered":"<p>Not everything needs a browser. If you&#8217;re monitoring prediction market odds from a headless VPS, an SSH session, or just prefer terminals over GUIs, a CLI monitor does the job in 50 lines of Python.<\/p>\n<p>In this tutorial, you&#8217;ll build a <strong>prediction market terminal<\/strong> that displays live odds from Polymarket, Kalshi, Pinnacle, and DraftKings in a color-coded table \u2014 with edge detection when prediction markets diverge from sharp sportsbooks. No browser, no Streamlit, no JavaScript.<\/p>\n<h2>Why CLI Over a Dashboard<\/h2>\n<p>The <a href=\"https:\/\/oddspapi.io\/blog\/prediction-market-dashboard-python-streamlit\/\">Streamlit dashboard<\/a> we built previously is great for visual analysis. But a terminal monitor has its own advantages:<\/p>\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>Feature<\/th>\n<th>Streamlit Dashboard<\/th>\n<th>CLI Terminal<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Runs on headless VPS<\/td>\n<td>Requires browser or tunneling<\/td>\n<td>\u2705 SSH-native<\/td>\n<\/tr>\n<tr>\n<td>Resource usage<\/td>\n<td>~100MB+ (browser + Python)<\/td>\n<td>~20MB (Python only)<\/td>\n<\/tr>\n<tr>\n<td>Composable<\/td>\n<td>Standalone web app<\/td>\n<td>\u2705 Pipe to files, cron, alerts<\/td>\n<\/tr>\n<tr>\n<td>Setup<\/td>\n<td>pip install streamlit plotly<\/td>\n<td>pip install rich requests<\/td>\n<\/tr>\n<tr>\n<td>Best for<\/td>\n<td>Visual analysis, sharing<\/td>\n<td>Monitoring, logging, automation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>If you&#8217;re running a trading bot on a $5\/month VPS, you don&#8217;t want to spin up a browser to check odds. You want <code>python monitor.py<\/code> and a clean table in your terminal.<\/p>\n<h2>What We&#8217;re Building<\/h2>\n<p>A Rich-powered terminal app that:<\/p>\n<ul>\n<li>Fetches upcoming fixtures from OddsPapi<\/li>\n<li>Displays Polymarket vs Pinnacle vs Bet365 vs DraftKings implied probabilities<\/li>\n<li>Color-codes edges (green = Polymarket lower than sharp, red = Polymarket higher)<\/li>\n<li>Auto-refreshes with Rich Live display<\/li>\n<li>Runs in any terminal \u2014 local, SSH, tmux, screen<\/li>\n<\/ul>\n<h2>Step 1: Install Dependencies<\/h2>\n<pre class=\"wp-block-code\"><code>pip install rich requests<\/code><\/pre>\n<p>Two packages. That&#8217;s it. <a href=\"https:\/\/rich.readthedocs.io\/\">Rich<\/a> handles the colored tables and live refresh. Requests handles the API calls.<\/p>\n<h2>Step 2: Fetch Prediction Market Odds<\/h2>\n<p>Same API calls as the dashboard tutorial \u2014 OddsPapi returns Polymarket, Kalshi, Pinnacle, and 350+ other bookmakers in a single response.<\/p>\n<pre class=\"wp-block-code\"><code>import requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE = \"https:\/\/api.oddspapi.io\/v4\"\n\ndef fetch_fixtures(sport_id=10, tournament_id=7, limit=20):\n    \"\"\"Fetch upcoming fixtures. Default: Champions League soccer.\"\"\"\n    params = {\"apiKey\": API_KEY, \"sportId\": sport_id,\n              \"tournamentId\": tournament_id, \"limit\": limit}\n    r = requests.get(f\"{BASE}\/fixtures\", params=params, timeout=15)\n    r.raise_for_status()\n    return r.json()\n\ndef fetch_odds(fixture_id):\n    \"\"\"Fetch odds from all bookmakers for a fixture.\"\"\"\n    r = requests.get(f\"{BASE}\/odds\",\n                     params={\"apiKey\": API_KEY, \"fixtureId\": fixture_id},\n                     timeout=15)\n    r.raise_for_status()\n    return r.json().get(\"bookmakerOdds\", {})<\/code><\/pre>\n<h2>Step 3: Build the Edge Detection Logic<\/h2>\n<p>The edge calculation is simple: compare Polymarket&#8217;s implied probability against Pinnacle&#8217;s sharp line. A positive difference means Polymarket thinks the outcome is more likely than the sharps do.<\/p>\n<pre class=\"wp-block-code\"><code>BOOKS = [\"polymarket\", \"pinnacle\", \"bet365\", \"draftkings\"]\nMARKET_ID = \"101\"  # Full Time Result (1X2) for soccer\nOUTCOMES = {\"101\": \"Home\", \"102\": \"Draw\", \"103\": \"Away\"}\n\ndef implied(price):\n    \"\"\"Convert decimal odds to implied probability.\"\"\"\n    return round(1 \/ price * 100, 1) if price and price > 0 else None\n\ndef get_probs(bo, slug):\n    \"\"\"Extract implied probabilities for a bookmaker.\"\"\"\n    if slug not in bo:\n        return {}\n    market = bo[slug].get(\"markets\", {}).get(MARKET_ID, {})\n    outs = market.get(\"outcomes\", {})\n    probs = {}\n    for oid, label in OUTCOMES.items():\n        price = (outs.get(oid, {}).get(\"players\", {})\n                 .get(\"0\", {}).get(\"price\"))\n        p = implied(price)\n        if p:\n            probs[label] = p\n    return probs\n\ndef calc_edge(poly_prob, pin_prob):\n    \"\"\"Calculate edge: Polymarket vs Pinnacle divergence.\"\"\"\n    if poly_prob is None or pin_prob is None:\n        return None\n    return round(poly_prob - pin_prob, 1)<\/code><\/pre>\n<h2>Step 4: Build the Rich Table Display<\/h2>\n<p>Rich tables support per-cell styling, which makes edge detection visual \u2014 green for opportunities where Polymarket underprices vs sharps, red for where it overprices.<\/p>\n<pre class=\"wp-block-code\"><code>from rich.table import Table\nfrom rich.text import Text\n\nEDGE_THRESHOLD = 5.0\n\ndef build_table(fixtures_data):\n    \"\"\"Build a Rich table comparing prediction market vs sportsbook odds.\"\"\"\n    table = Table(title=\"Prediction Market Monitor\", show_lines=True)\n    table.add_column(\"Match\", style=\"bold white\", min_width=30)\n    table.add_column(\"Outcome\", style=\"cyan\")\n    table.add_column(\"Polymarket\", justify=\"right\")\n    table.add_column(\"Pinnacle\", justify=\"right\")\n    table.add_column(\"Bet365\", justify=\"right\")\n    table.add_column(\"DraftKings\", justify=\"right\")\n    table.add_column(\"Edge (pp)\", justify=\"right\")\n\n    for match_name, bo in fixtures_data:\n        poly = get_probs(bo, \"polymarket\")\n        pin = get_probs(bo, \"pinnacle\")\n        b365 = get_probs(bo, \"bet365\")\n        dk = get_probs(bo, \"draftkings\")\n\n        if not poly or not pin:\n            continue\n\n        for label in [\"Home\", \"Draw\", \"Away\"]:\n            edge = calc_edge(poly.get(label), pin.get(label))\n            edge_text = Text(f\"{edge:+.1f}\" if edge else \"\u2014\")\n            if edge and abs(edge) >= EDGE_THRESHOLD:\n                edge_text.stylize(\"bold red\" if edge > 0 else \"bold green\")\n\n            table.add_row(\n                match_name if label == \"Home\" else \"\",\n                label,\n                f\"{poly.get(label, '\u2014')}%\",\n                f\"{pin.get(label, '\u2014')}%\",\n                f\"{b365.get(label, '\u2014')}%\",\n                f\"{dk.get(label, '\u2014')}%\",\n                edge_text,\n            )\n\n    return table<\/code><\/pre>\n<h2>Step 5: Complete Monitor with Auto-Refresh<\/h2>\n<p>Here&#8217;s the complete monitor. Save this as <code>monitor.py<\/code>:<\/p>\n<pre class=\"wp-block-code\"><code>import time, requests\nfrom rich.live import Live\nfrom rich.table import Table\nfrom rich.text import Text\nfrom rich.console import Console\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE = \"https:\/\/api.oddspapi.io\/v4\"\nMARKET_ID = \"101\"  # 1X2 for soccer, use \"111\" for NBA moneyline\nOUTCOMES = {\"101\": \"Home\", \"102\": \"Draw\", \"103\": \"Away\"}\nEDGE_THRESHOLD = 5.0\nREFRESH = 60  # seconds\n\n\ndef implied(price):\n    return round(1 \/ price * 100, 1) if price and price > 0 else None\n\n\ndef get_probs(bo, slug):\n    if slug not in bo:\n        return {}\n    market = bo[slug].get(\"markets\", {}).get(MARKET_ID, {})\n    outs = market.get(\"outcomes\", {})\n    return {label: implied(outs.get(oid, {}).get(\"players\", {})\n            .get(\"0\", {}).get(\"price\"))\n            for oid, label in OUTCOMES.items()\n            if implied(outs.get(oid, {}).get(\"players\", {})\n            .get(\"0\", {}).get(\"price\"))}\n\n\ndef fetch_data():\n    params = {\"apiKey\": API_KEY, \"sportId\": 10,\n              \"tournamentId\": 7, \"limit\": 300}\n    fxs = requests.get(f\"{BASE}\/fixtures\", params=params, timeout=15).json()\n    now = time.strftime(\"%Y-%m-%dT%H:%M:%S\")\n    fxs = [f for f in fxs if f.get(\"startTime\", \"\") >= now][:10]\n    data = []\n    for fx in fxs:\n        bo = requests.get(f\"{BASE}\/odds\",\n                          params={\"apiKey\": API_KEY,\n                                  \"fixtureId\": fx[\"fixtureId\"]},\n                          timeout=15).json().get(\"bookmakerOdds\", {})\n        if \"polymarket\" in bo:\n            name = f\"{fx['participant1Name']} vs {fx['participant2Name']}\"\n            data.append((name, bo))\n    return data\n\n\ndef build_table(data):\n    t = Table(title=f\"Prediction Market Monitor \u2014 \"\n              f\"{time.strftime('%H:%M:%S')}\", show_lines=True)\n    t.add_column(\"Match\", style=\"bold white\", min_width=28)\n    t.add_column(\"Outcome\", style=\"cyan\")\n    t.add_column(\"Polymarket\", justify=\"right\")\n    t.add_column(\"Pinnacle\", justify=\"right\")\n    t.add_column(\"Bet365\", justify=\"right\")\n    t.add_column(\"DraftKings\", justify=\"right\")\n    t.add_column(\"Edge\", justify=\"right\")\n\n    for name, bo in data:\n        poly = get_probs(bo, \"polymarket\")\n        pin = get_probs(bo, \"pinnacle\")\n        b365 = get_probs(bo, \"bet365\")\n        dk = get_probs(bo, \"draftkings\")\n        if not poly or not pin:\n            continue\n        for label in OUTCOMES.values():\n            pp, pn = poly.get(label), pin.get(label)\n            edge = round(pp - pn, 1) if pp and pn else None\n            et = Text(f\"{edge:+.1f}pp\" if edge else \"\u2014\")\n            if edge and abs(edge) >= EDGE_THRESHOLD:\n                et.stylize(\"bold red\" if edge > 0 else \"bold green\")\n            t.add_row(\n                name if label == list(OUTCOMES.values())[0] else \"\",\n                label,\n                f\"{pp}%\" if pp else \"\u2014\",\n                f\"{pn}%\" if pn else \"\u2014\",\n                f\"{b365.get(label, '\u2014') if b365 else '\u2014'}%\"\n                    if b365.get(label) else \"\u2014\",\n                f\"{dk.get(label, '\u2014') if dk else '\u2014'}%\"\n                    if dk.get(label) else \"\u2014\",\n                et,\n            )\n    return t\n\n\nconsole = Console()\nconsole.print(\"[bold]Starting prediction market monitor...[\/]\")\nconsole.print(f\"[dim]Refresh: {REFRESH}s | Edge threshold: \"\n              f\"{EDGE_THRESHOLD}pp | Ctrl+C to quit[\/]\\n\")\n\nwith Live(console=console, refresh_per_second=1) as live:\n    while True:\n        try:\n            data = fetch_data()\n            live.update(build_table(data))\n            time.sleep(REFRESH)\n        except KeyboardInterrupt:\n            break\n        except Exception as e:\n            console.print(f\"[red]Error: {e}[\/]\")\n            time.sleep(10)<\/code><\/pre>\n<h2>Running the Monitor<\/h2>\n<pre class=\"wp-block-code\"><code>python monitor.py<\/code><\/pre>\n<p>You&#8217;ll see a table like this in your terminal:<\/p>\n<pre class=\"wp-block-code\"><code>\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Match                        \u2502 Outcome \u2502 Polymarket  \u2502 Pinnacle \u2502 Bet365 \u2502 DraftKings \u2502 Edge     \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 Galatasaray vs Liverpool     \u2502 Home    \u2502 31.5%       \u2502 21.7%    \u2502 22.2%  \u2502 22.0%      \u2502 +9.8pp   \u2502\n\u2502                              \u2502 Draw    \u2502 19.5%       \u2502 24.0%    \u2502 23.8%  \u2502 24.4%      \u2502 -4.5pp   \u2502\n\u2502                              \u2502 Away    \u2502 56.0%       \u2502 57.6%    \u2502 55.6%  \u2502 58.8%      \u2502 -1.6pp   \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 Real Madrid vs Man City      \u2502 Home    \u2502 36.0%       \u2502 28.6%    \u2502 29.4%  \u2502 28.6%      \u2502 +7.4pp   \u2502\n\u2502                              \u2502 Draw    \u2502 26.0%       \u2502 25.3%    \u2502 24.4%  \u2502 25.6%      \u2502 +0.7pp   \u2502\n\u2502                              \u2502 Away    \u2502 41.0%       \u2502 49.5%    \u2502 47.6%  \u2502 50.0%      \u2502 -8.5pp   \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518<\/code><\/pre>\n<p>Red edges mean Polymarket overprices the outcome vs Pinnacle. Green edges mean Polymarket underprices it. The table refreshes every 60 seconds without clearing your terminal history.<\/p>\n<h2>Bonus: Pipe to a Log File<\/h2>\n<p>Since this is a CLI tool, you can pipe the output to a file for historical tracking:<\/p>\n<pre class=\"wp-block-code\"><code># Log edges to a file every minute\npython monitor.py 2>&amp;1 | tee -a prediction_edges.log<\/code><\/pre>\n<p>Or run it in a tmux session on your VPS:<\/p>\n<pre class=\"wp-block-code\"><code>tmux new -s monitor\npython monitor.py\n# Ctrl+B, D to detach \u2014 it keeps running<\/code><\/pre>\n<h2>Adapting for NBA<\/h2>\n<p>To monitor NBA moneylines instead of soccer 1X2, change the config at the top:<\/p>\n<pre class=\"wp-block-code\"><code># NBA configuration\nMARKET_ID = \"111\"  # Moneyline\nOUTCOMES = {\"111\": \"Home\", \"112\": \"Away\"}\n\n# In fetch_data(), change:\nparams = {\"apiKey\": API_KEY, \"sportId\": 11,  # Basketball\n          \"tournamentId\": 132,               # NBA\n          \"limit\": 300}<\/code><\/pre>\n<p>Polymarket covers NBA moneylines on every game \u2014 you&#8217;ll see the same kind of divergences between crowd pricing and sharp books.<\/p>\n<h2>Dashboard vs Terminal: When to Use Each<\/h2>\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>Scenario<\/th>\n<th>Use Dashboard<\/th>\n<th>Use Terminal<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Visual analysis of odds<\/td>\n<td>\u2705 Plotly charts<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Sharing with team<\/td>\n<td>\u2705 Browser-based<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Headless VPS monitoring<\/td>\n<td><\/td>\n<td>\u2705 SSH-native<\/td>\n<\/tr>\n<tr>\n<td>Cron job \/ automation<\/td>\n<td><\/td>\n<td>\u2705 No browser needed<\/td>\n<\/tr>\n<tr>\n<td>Low resource usage<\/td>\n<td><\/td>\n<td>\u2705 20MB vs 100MB+<\/td>\n<\/tr>\n<tr>\n<td>Piping to log files<\/td>\n<td><\/td>\n<td>\u2705 stdout friendly<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>Both tools hit the same OddsPapi API. The data is identical \u2014 it&#8217;s just how you view it.<\/p>\n<h2>Why OddsPapi for Terminal Monitoring<\/h2>\n<p>This monitor works because OddsPapi aggregates <strong>350+ bookmakers<\/strong> \u2014 including prediction market exchanges like Polymarket and Kalshi \u2014 into a single API. You don&#8217;t need separate integrations for each source. One API key, one request, and you get every price from every bookmaker on the fixture.<\/p>\n<p>The free tier includes historical data for backtesting, and when you need real-time feeds with sub-second latency, upgrade to <a href=\"https:\/\/oddspapi.io\/blog\/websocket-odds-api-real-time-betting-data\/\">WebSockets<\/a>.<\/p>\n<h2>Build Your Monitor in 10 Minutes<\/h2>\n<p>50 lines of Python, two pip packages, and one API key. That&#8217;s all you need to monitor prediction market edges from any terminal in the world.<\/p>\n<p><strong><a href=\"https:\/\/oddspapi.io\/en\/register\">Get your free API key<\/a><\/strong> and run <code>python monitor.py<\/code>. If you prefer charts over tables, check out our <a href=\"https:\/\/oddspapi.io\/blog\/prediction-market-dashboard-python-streamlit\/\">Streamlit dashboard tutorial<\/a> instead.<\/p>\n<p><!--\nFocus Keyphrase: prediction market terminal\nSEO Title: Prediction Market Terminal: Build a CLI Trading Monitor with Python\nMeta Description: Build a CLI prediction market monitor with Python and Rich. Compare Polymarket odds vs Pinnacle, Bet365 and 350+ bookmakers from any terminal.\nSlug: prediction-market-terminal-python-cli\n--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Build a CLI prediction market monitor with Python and Rich. Compare Polymarket odds vs Pinnacle, Bet365 and 350+ bookmakers from any terminal.<\/p>\n","protected":false},"author":2,"featured_media":2515,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[8,9,14,11],"class_list":["post-2486","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-how-to-guides","tag-free-api","tag-odds-api","tag-polymarket","tag-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Prediction Market Terminal: Build a CLI Trading Monitor with Python | Odds API Development Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/oddspapi.io\/blog\/prediction-market-terminal-python-cli\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prediction Market Terminal: Build a CLI Trading Monitor with Python | Odds API Development Blog\" \/>\n<meta property=\"og:description\" content=\"Build a CLI prediction market monitor with Python and Rich. 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