Metrics

analyze.py

@@ -29,6 +29,7 @@ import math
 import sqlite3
 import logging
 import warnings
+from datetime import datetime
 from pathlib import Path
 
 # Silence transformer noise before importing
@@ -78,6 +79,37 @@ MIN_COVERAGE_SPREAD = 0.0   # disabled: signal_score naturally zeros out single-
 FINBERT_MIN_CONF    = 0.70  # drop neutral articles below this FinBERT confidence
 ALERT_THRESHOLD     = 0.35  # signal_score > this → alert
 
+# ---------------------------------------------------------------------------
+# Claude metrics
+# ---------------------------------------------------------------------------
+
+METRICS_FILE = Path(__file__).parent / "metrics.json"
+
+# Pricing: Claude Haiku 4.5 — https://www.anthropic.com/pricing
+_PRICE_INPUT_PER_TOKEN  = 0.80 / 1_000_000   # $0.80 per MTok
+_PRICE_OUTPUT_PER_TOKEN = 4.00 / 1_000_000   # $4.00 per MTok
+
+
+def calc_cost(input_tokens: int, output_tokens: int) -> float:
+    return round(input_tokens * _PRICE_INPUT_PER_TOKEN + output_tokens * _PRICE_OUTPUT_PER_TOKEN, 6)
+
+
+def update_metrics(input_tokens: int, output_tokens: int) -> None:
+    """Accumulate Claude token usage into metrics.json."""
+    cost = calc_cost(input_tokens, output_tokens)
+    data: dict = {}
+    if METRICS_FILE.exists():
+        try:
+            data = json.loads(METRICS_FILE.read_text())
+        except Exception:
+            pass
+    data["total_calls"]         = data.get("total_calls", 0) + 1
+    data["total_input_tokens"]  = data.get("total_input_tokens", 0) + input_tokens
+    data["total_output_tokens"] = data.get("total_output_tokens", 0) + output_tokens
+    data["total_cost_usd"]      = round(data.get("total_cost_usd", 0.0) + cost, 6)
+    data["last_updated"]        = datetime.now().isoformat(timespec="seconds")
+    METRICS_FILE.write_text(json.dumps(data, indent=2))
+
 
 # ---------------------------------------------------------------------------
 # Model loading
@@ -195,16 +227,16 @@ def coverage_spread_score(row) -> float:
 # Claude structured extraction
 # ---------------------------------------------------------------------------
 
-def claude_extract(title: str, text: str, tickers: list[str]) -> dict:
+def claude_extract(title: str, text: str, tickers: list[str]) -> tuple[dict, int, int]:
     """
     Use Claude Haiku to extract structured financial signal.
-    Returns {"confirmed_tickers", "magnitude", "timeframe", "reasoning"}.
+    Returns ({"confirmed_tickers", "magnitude", "timeframe", "reasoning"}, input_tokens, output_tokens).
     """
     import anthropic
 
     api_key = os.environ.get("ANTHROPIC_API_KEY")
     if not api_key:
-        return {"confirmed_tickers": tickers, "magnitude": 5, "timeframe": "days", "reasoning": "(no API key)"}
+        return {"confirmed_tickers": tickers, "magnitude": 5, "timeframe": "days", "reasoning": "(no API key)"}, 0, 0
 
     client = anthropic.Anthropic(api_key=api_key)
     ticker_ctx = "\n".join(
@@ -245,10 +277,10 @@ Fields:
         raw = msg.content[0].text.strip()
         raw = re.sub(r"^```(?:json)?\n?", "", raw)
         raw = re.sub(r"\n?```$", "", raw)
-        return json.loads(raw)
+        return json.loads(raw), msg.usage.input_tokens, msg.usage.output_tokens
     except Exception as e:
         print(f"  [warn] Claude failed: {e}")
-        return {"confirmed_tickers": tickers, "magnitude": 5, "timeframe": "days", "reasoning": str(e)[:120]}
+        return {"confirmed_tickers": tickers, "magnitude": 5, "timeframe": "days", "reasoning": str(e)[:120]}, 0, 0
 
 
 # ---------------------------------------------------------------------------
@@ -476,7 +508,9 @@ def analyze_articles(
         claude_data: dict = {}
         if use_claude and not dry_run and os.environ.get("ANTHROPIC_API_KEY"):
             print(f"  [claude] {slug[:50]}")
-            claude_data = claude_extract(title, full_text, list(matches.keys()))
+            claude_data, _in_tok, _out_tok = claude_extract(title, full_text, list(matches.keys()))
+            if _in_tok:
+                update_metrics(_in_tok, _out_tok)
 
         # ------------------------------------------------------------------
         # Phase 6 — yfinance momentum + scoring

dashboard.py

@@ -28,10 +28,281 @@ from report import _c25_day_return, _unrealised_pnl, _realised_pnl, _total_fees,
 
 CAPITAL      = 10_000
 LOG_DIR      = Path(os.getenv("LOG_DIR", str(Path(__file__).parent / "logs")))
+METRICS_FILE = Path(__file__).parent / "metrics.json"
+REFRESH      = 60   # seconds
+
+CAPITAL      = 10_000
+LOG_DIR      = Path(os.getenv("LOG_DIR", str(Path(__file__).parent / "logs")))
+METRICS_FILE = Path(__file__).parent / "metrics.json"
 REFRESH      = 60   # seconds
 
 app = Flask(__name__)
 
+# ── Metrics dashboard template ────────────────────────────────────────────────
+
+METRICS_TEMPLATE = """<!DOCTYPE html>
+<html lang="da">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>MoneyMaker · AI Costs</title>
+  <link rel="preconnect" href="https://fonts.googleapis.com">
+  <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
+  <script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script>
+  <style>
+    :root {
+      --bg:        #eef2f7;
+      --surface:   #ffffff;
+      --border:    #e2e8f0;
+      --text:      #0f172a;
+      --muted:     #64748b;
+      --accent:    #2563eb;
+      --green:     #16a34a;
+      --red:       #dc2626;
+      --yellow:    #d97706;
+      --purple:    #7c3aed;
+      --shadow-sm: 0 1px 2px rgba(0,0,0,.05);
+      --shadow:    0 1px 3px rgba(0,0,0,.07), 0 1px 2px rgba(0,0,0,.04);
+    }
+    * { box-sizing: border-box; margin: 0; padding: 0; }
+    body {
+      font-family: 'Inter', system-ui, sans-serif;
+      background: var(--bg);
+      color: var(--text);
+      padding: 24px 32px 52px;
+      min-height: 100vh;
+    }
+    .header {
+      display: flex; justify-content: space-between; align-items: center;
+      margin-bottom: 22px; padding-bottom: 18px;
+      border-bottom: 1px solid var(--border);
+    }
+    .header-left { display: flex; align-items: center; gap: 12px; }
+    .header h1 { font-size: 1.25rem; font-weight: 800; letter-spacing: -.03em; }
+    .header h1 span { color: var(--accent); }
+    .header-pill {
+      font-size: 0.65rem; font-weight: 700; text-transform: uppercase; letter-spacing: .08em;
+      color: var(--purple); background: rgba(124,58,237,.09);
+      padding: 3px 9px; border-radius: 20px; border: 1px solid rgba(124,58,237,.18);
+    }
+    .header-meta { font-size: 0.7rem; color: var(--muted); text-align: right; line-height: 1.9; }
+    .back-link {
+      font-size: 0.72rem; color: var(--accent); text-decoration: none; font-weight: 600;
+      display: inline-flex; align-items: center; gap: 4px;
+    }
+    .back-link:hover { text-decoration: underline; }
+
+    /* KPI strip */
+    .kpis { display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 18px; }
+    @media (max-width: 1000px) { .kpis { grid-template-columns: repeat(3, 1fr); } }
+    @media (max-width: 600px)  { .kpis { grid-template-columns: repeat(2, 1fr); } }
+    .kpi {
+      background: var(--surface);
+      border-radius: 10px; padding: 15px 16px;
+      box-shadow: var(--shadow);
+      border-top: 3px solid transparent;
+    }
+    .kpi.blue   { border-top-color: var(--accent); }
+    .kpi.green  { border-top-color: var(--green); }
+    .kpi.red    { border-top-color: var(--red); }
+    .kpi.yellow { border-top-color: var(--yellow); }
+    .kpi.purple { border-top-color: var(--purple); }
+    .kpi-label { font-size: 0.64rem; font-weight: 700; text-transform: uppercase; letter-spacing: .09em; color: var(--muted); margin-bottom: 9px; }
+    .kpi-value { font-size: 1.6rem; font-weight: 800; line-height: 1; letter-spacing: -.04em; font-family: 'JetBrains Mono', monospace; }
+    .kpi-value.sm { font-size: 1.15rem; }
+    .kpi-sub   { font-size: 0.71rem; color: var(--muted); margin-top: 6px; }
+
+    /* Charts row */
+    .charts { display: grid; grid-template-columns: 1fr 320px; gap: 10px; margin-bottom: 18px; }
+    @media (max-width: 900px) { .charts { grid-template-columns: 1fr; } }
+    .chart-card {
+      background: var(--surface); border-radius: 10px; box-shadow: var(--shadow);
+      padding: 14px 16px 8px;
+    }
+    .chart-card-title {
+      font-size: 0.64rem; font-weight: 700; text-transform: uppercase;
+      letter-spacing: .09em; color: var(--muted); margin-bottom: 4px;
+    }
+
+    /* Burn rate bar */
+    .burn-section { background: var(--surface); border-radius: 10px; box-shadow: var(--shadow); padding: 16px 20px; margin-bottom: 18px; }
+    .burn-title { font-size: 0.64rem; font-weight: 700; text-transform: uppercase; letter-spacing: .09em; color: var(--muted); margin-bottom: 14px; }
+    .burn-row { display: flex; align-items: center; gap: 12px; margin-bottom: 10px; }
+    .burn-label { font-size: 0.75rem; color: var(--muted); width: 130px; flex-shrink: 0; }
+    .burn-bar-bg { flex: 1; height: 8px; background: #f1f5f9; border-radius: 4px; overflow: hidden; }
+    .burn-bar-fg { height: 100%; border-radius: 4px; transition: width .6s ease; }
+    .burn-val { font-size: 0.72rem; font-family: 'JetBrains Mono', monospace; color: var(--text); width: 80px; text-align: right; flex-shrink: 0; }
+
+    /* Info table */
+    .section { background: var(--surface); border-radius: 10px; box-shadow: var(--shadow); overflow: hidden; }
+    .section-header {
+      padding: 11px 16px; font-size: 0.64rem; font-weight: 700;
+      text-transform: uppercase; letter-spacing: .09em;
+      color: var(--muted); background: #f8fafc;
+      border-bottom: 1px solid var(--border);
+    }
+    table { width: 100%; border-collapse: collapse; }
+    th { padding: 8px 14px; font-size: 0.63rem; font-weight: 700; text-transform: uppercase; letter-spacing: .08em; color: var(--muted); text-align: left; border-bottom: 1px solid var(--border); background: #f8fafc; }
+    td { padding: 10px 14px; font-size: 0.81rem; border-bottom: 1px solid #f1f5f9; }
+    tr:last-child td { border-bottom: none; }
+    tr:hover td { background: rgba(37,99,235,.025); }
+    td.mono { font-family: 'JetBrains Mono', monospace; font-size: 0.77rem; }
+    .empty { padding: 24px 16px; font-size: 0.8rem; color: var(--muted); font-style: italic; text-align: center; }
+
+    .footer { font-size: 0.64rem; color: #cbd5e1; text-align: center; margin-top: 32px; letter-spacing: .05em; }
+    .no-data { display: flex; flex-direction: column; align-items: center; justify-content: center; gap: 8px; padding: 60px 20px; }
+    .no-data-icon { font-size: 2.5rem; }
+    .no-data-text { font-size: 0.88rem; color: var(--muted); }
+  </style>
+</head>
+<body>
+
+<div class="header">
+  <div class="header-left">
+    <h1>Money<span>Maker</span></h1>
+    <span class="header-pill">AI Costs</span>
+  </div>
+  <div class="header-meta">
+    {{ now }}<br>
+    <a class="back-link" href="/">← Dashboard</a>
+  </div>
+</div>
+
+{% if no_data %}
+<div class="section">
+  <div class="no-data">
+    <div class="no-data-icon">📊</div>
+    <div class="no-data-text">Ingen data endnu — kør analyze.py med Claude for at begynde at tracke forbrug.</div>
+  </div>
+</div>
+{% else %}
+
+<div class="kpis">
+  <div class="kpi red">
+    <div class="kpi-label">Total Cost</div>
+    <div class="kpi-value{% if total_cost_usd >= 1 %} sm{% endif %}">${{ "%.4f"|format(total_cost_usd) }}</div>
+    <div class="kpi-sub">USD spent</div>
+  </div>
+  <div class="kpi blue">
+    <div class="kpi-label">Claude Calls</div>
+    <div class="kpi-value">{{ total_calls }}</div>
+    <div class="kpi-sub">articles analyzed</div>
+  </div>
+  <div class="kpi yellow">
+    <div class="kpi-label">Input Tokens</div>
+    <div class="kpi-value sm">{{ "{:,}".format(total_input_tokens) }}</div>
+    <div class="kpi-sub">${{ "%.4f"|format(cost_input) }} · {{ input_pct }}%</div>
+  </div>
+  <div class="kpi purple">
+    <div class="kpi-label">Output Tokens</div>
+    <div class="kpi-value sm">{{ "{:,}".format(total_output_tokens) }}</div>
+    <div class="kpi-sub">${{ "%.4f"|format(cost_output) }} · {{ output_pct }}%</div>
+  </div>
+  <div class="kpi green">
+    <div class="kpi-label">Cost / Article</div>
+    <div class="kpi-value{% if cost_per_call >= 0.01 %} sm{% endif %}">${{ "%.5f"|format(cost_per_call) }}</div>
+    <div class="kpi-sub">avg per Claude call</div>
+  </div>
+</div>
+
+<div class="charts">
+  <div class="chart-card">
+    <div class="chart-card-title">Token Distribution — Input vs Output</div>
+    <div id="bar-chart" style="height:220px;"></div>
+  </div>
+  <div class="chart-card">
+    <div class="chart-card-title">Cost Split</div>
+    <div id="donut-chart" style="height:220px;"></div>
+  </div>
+</div>
+
+<div class="burn-section">
+  <div class="burn-title">Token Breakdown</div>
+  <div class="burn-row">
+    <div class="burn-label">Input tokens</div>
+    <div class="burn-bar-bg"><div class="burn-bar-fg" style="width:{{ input_pct }}%; background: var(--yellow);"></div></div>
+    <div class="burn-val">{{ "{:,}".format(total_input_tokens) }}</div>
+  </div>
+  <div class="burn-row">
+    <div class="burn-label">Output tokens</div>
+    <div class="burn-bar-bg"><div class="burn-bar-fg" style="width:{{ output_pct }}%; background: var(--purple);"></div></div>
+    <div class="burn-val">{{ "{:,}".format(total_output_tokens) }}</div>
+  </div>
+  <div class="burn-row">
+    <div class="burn-label">Total tokens</div>
+    <div class="burn-bar-bg"><div class="burn-bar-fg" style="width:100%; background: var(--accent);"></div></div>
+    <div class="burn-val">{{ "{:,}".format(total_input_tokens + total_output_tokens) }}</div>
+  </div>
+</div>
+
+<div class="section">
+  <div class="section-header">Model Details</div>
+  <table>
+    <thead>
+      <tr>
+        <th>Metric</th>
+        <th>Value</th>
+        <th>Note</th>
+      </tr>
+    </thead>
+    <tbody>
+      <tr><td>Model</td><td class="mono">claude-haiku-4-5</td><td>Anthropic</td></tr>
+      <tr><td>Input price</td><td class="mono">$0.80 / MTok</td><td>${{ "%.6f"|format(0.80/1_000_000) }} per token</td></tr>
+      <tr><td>Output price</td><td class="mono">$4.00 / MTok</td><td>${{ "%.6f"|format(4.00/1_000_000) }} per token</td></tr>
+      <tr><td>Total calls</td><td class="mono">{{ total_calls }}</td><td>articles sent to Claude</td></tr>
+      <tr><td>Total input tokens</td><td class="mono">{{ "{:,}".format(total_input_tokens) }}</td><td>cost ${{ "%.5f"|format(cost_input) }}</td></tr>
+      <tr><td>Total output tokens</td><td class="mono">{{ "{:,}".format(total_output_tokens) }}</td><td>cost ${{ "%.5f"|format(cost_output) }}</td></tr>
+      <tr><td>Total cost</td><td class="mono">${{ "%.6f"|format(total_cost_usd) }}</td><td>cumulative</td></tr>
+      <tr><td>Last updated</td><td class="mono">{{ last_updated }}</td><td></td></tr>
+    </tbody>
+  </table>
+</div>
+
+<script>
+// ── Bar chart: tokens per type ──────────────────────────────────────────────
+const barChart = echarts.init(document.getElementById('bar-chart'));
+barChart.setOption({
+  grid: { top: 24, right: 20, bottom: 28, left: 70 },
+  tooltip: { trigger: 'axis', formatter: p => p[0].name + '<br/>' + p[0].marker + ' ' + p[0].seriesName + ': <b>' + p[0].value.toLocaleString() + '</b>' },
+  xAxis: { type: 'category', data: ['Input', 'Output'], axisLabel: { fontSize: 11 } },
+  yAxis: { type: 'value', axisLabel: { fontSize: 10, formatter: v => v >= 1000 ? (v/1000).toFixed(0)+'k' : v } },
+  series: [{
+    name: 'Tokens',
+    type: 'bar', barMaxWidth: 60,
+    data: [
+      { value: {{ total_input_tokens }},  itemStyle: { color: '#d97706', borderRadius: [4,4,0,0] } },
+      { value: {{ total_output_tokens }}, itemStyle: { color: '#7c3aed', borderRadius: [4,4,0,0] } },
+    ],
+    label: { show: true, position: 'top', formatter: p => p.value.toLocaleString(), fontSize: 10, color: '#64748b' }
+  }]
+});
+
+// ── Donut: cost split ───────────────────────────────────────────────────────
+const donut = echarts.init(document.getElementById('donut-chart'));
+donut.setOption({
+  tooltip: { trigger: 'item', formatter: '{b}: ${c} ({d}%)' },
+  legend: { bottom: 0, left: 'center', textStyle: { fontSize: 10, color: '#64748b' } },
+  series: [{
+    type: 'pie', radius: ['42%', '68%'],
+    center: ['50%', '44%'],
+    label: { show: false },
+    emphasis: { label: { show: true, fontSize: 12, fontWeight: 'bold' } },
+    data: [
+      { value: {{ "%.6f"|format(cost_input) }},  name: 'Input',  itemStyle: { color: '#d97706' } },
+      { value: {{ "%.6f"|format(cost_output) }}, name: 'Output', itemStyle: { color: '#7c3aed' } },
+    ]
+  }]
+});
+
+window.addEventListener('resize', () => { barChart.resize(); donut.resize(); });
+</script>
+{% endif %}
+
+<div class="footer">MoneyMaker · claude-haiku-4-5 · metrics updated on each analyze run</div>
+</body>
+</html>
+"""
+
 # ── HTML template ─────────────────────────────────────────────────────────────
 
 TEMPLATE = """<!DOCTYPE html>
@@ -762,6 +1033,69 @@ def health():
     return {"status": "ok", "db": DB_TYPE, "ts": time.time()}
 
 
+@app.route("/metrics")
+def metrics():
+    data: dict = {}
+    if METRICS_FILE.exists():
+        try:
+            data = json.loads(METRICS_FILE.read_text())
+        except Exception:
+            pass
+
+    # Total articles with Claude signal from DB
+    try:
+        db = get_conn()
+        row = db.execute(
+            "SELECT COUNT(DISTINCT article_slug) AS cnt FROM article_signals WHERE claude_reasoning != '' AND claude_reasoning IS NOT NULL"
+        ).fetchone()
+        data["total_articles_with_claude"] = row[0] if row else 0
+        db.close()
+    except Exception:
+        pass
+
+    return jsonify(data)
+
+
+@app.route("/metrics-dash")
+def metrics_dash():
+    data: dict = {}
+    if METRICS_FILE.exists():
+        try:
+            data = json.loads(METRICS_FILE.read_text())
+        except Exception:
+            pass
+
+    total_calls         = data.get("total_calls", 0)
+    total_input_tokens  = data.get("total_input_tokens", 0)
+    total_output_tokens = data.get("total_output_tokens", 0)
+    total_cost_usd      = data.get("total_cost_usd", 0.0)
+    last_updated        = data.get("last_updated", "—")
+
+    total_tokens = total_input_tokens + total_output_tokens
+    input_pct  = round(total_input_tokens  / total_tokens * 100) if total_tokens else 0
+    output_pct = round(total_output_tokens / total_tokens * 100) if total_tokens else 0
+
+    cost_input  = round(total_input_tokens  * 0.80 / 1_000_000, 6)
+    cost_output = round(total_output_tokens * 4.00 / 1_000_000, 6)
+    cost_per_call = round(total_cost_usd / total_calls, 6) if total_calls else 0.0
+
+    return render_template_string(
+        METRICS_TEMPLATE,
+        no_data=total_calls == 0,
+        now=datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC"),
+        total_calls=total_calls,
+        total_input_tokens=total_input_tokens,
+        total_output_tokens=total_output_tokens,
+        total_cost_usd=total_cost_usd,
+        last_updated=last_updated,
+        input_pct=input_pct,
+        output_pct=output_pct,
+        cost_input=cost_input,
+        cost_output=cost_output,
+        cost_per_call=cost_per_call,
+    )
+
+
 # ── Main ──────────────────────────────────────────────────────────────────────
 
 def main():