trading/services/meet_kevin_watcher/llm_analyzer.py

"""Anthropic SDK LLM analyzer for Meet Kevin video transcripts.

Calls Claude Sonnet (via native Anthropic SDK with OAuth bearer token) with
tool-use forcing to extract structured MeetKevinAnalysis from a video transcript.

Public API:
  SYSTEM_PROMPT         — module-level analyst instructions
  compute_cost_usd()    — Decimal-precise cost from token counts
  LlmCallResult         — frozen dataclass returned by analyze()
  LlmAnalyzer           — async class; .analyze() does the API call
"""

import logging
from dataclasses import dataclass
from datetime import datetime
from decimal import Decimal
from typing import Any

from anthropic import AsyncAnthropic

from shared.schemas.meet_kevin import MeetKevinAnalysis

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Pricing table  (USD per 1 000 000 tokens: input, output)
# Native Anthropic list pricing.  With OAuth/Enterprise tokens real billing
# is via subscription quota, but we still compute notional USD for the
# daily-cap accounting logic.
# ---------------------------------------------------------------------------

_PRICING: dict[str, tuple[Decimal, Decimal]] = {
    "claude-sonnet-4-5": (Decimal("3"), Decimal("15")),
    "claude-sonnet-4-6": (Decimal("3"), Decimal("15")),
    "claude-opus-4-7": (Decimal("15"), Decimal("75")),
    "claude-haiku-4-5-20251001": (Decimal("1"), Decimal("5")),
}

# ---------------------------------------------------------------------------
# System prompt
# ---------------------------------------------------------------------------

SYSTEM_PROMPT = """
You are a professional financial analyst specialising in retail investor sentiment.
Your task is to read the full transcript of a Meet Kevin (Kevin Paffrath) YouTube
video and extract a structured investment analysis from it.

## Your mission

Read the transcript carefully and produce a single, precise call to the
`submit_analysis` tool. Do **not** respond with prose — your entire output must be
that one tool call with all required fields filled in correctly.

## What to extract

### Market outlook
Identify the overall market direction Kevin is expressing: bullish, bearish, neutral,
or mixed. Write a concise `market_outlook_reasoning` (2–4 sentences) that explains
*why* you assigned that direction, grounded in specific statements from the video.

### Macro themes
List the 2–6 highest-level economic or policy themes Kevin discusses (e.g.
"Federal Reserve rate path", "AI capex cycle", "commercial real estate stress",
"dollar strength", "energy transition"). These should be phrase-length labels, not
full sentences.

### Key risks
List the 2–5 principal downside risks Kevin flags. Again, short phrase labels, not
paragraphs. Only include risks Kevin explicitly names or clearly implies — do not
invent risks he did not discuss.

### Summary
Write a ~200-word plain-English summary of the video's investment thesis. Focus on
actionable takeaways and any specific catalysts Kevin mentions. Avoid filler phrases
like "In this video Kevin discusses…" — start directly with the insight.

### Per-ticker mentions (tickers field)
Extract every stock, ETF, or crypto ticker that Kevin makes a substantive statement
about. For each one, fill in the following:

- **symbol** — The uppercase ticker symbol (e.g. "NVDA", "SPY", "BTC"). If Kevin
  mentions the company name but not the ticker, infer the ticker from the name (e.g.
  "Nvidia" → "NVDA"). Max 6 characters. Only include tickers you are confident about.

- **action** — The clearest action signal you can infer from what Kevin says. Use
  exactly one of: `buy`, `sell`, `hold`, `watch`, `avoid`. If Kevin expresses
  interest but no clear directional view, use `watch`. If he says he is exiting or
  would not touch it, use `sell` or `avoid` respectively. Do not default to `hold`
  just because you are unsure — skip the ticker instead.

- **conviction** — A float between 0.0 and 1.0 representing how confident Kevin
  sounds. Use 0.8–1.0 for "I'm buying this aggressively / this is my top pick",
  0.5–0.7 for a clear directional view with some hedging, 0.2–0.4 for a tentative
  or heavily-caveated take. A ticker Kevin mentions only in passing (< 20 words of
  commentary) should be **skipped entirely** rather than assigned low conviction.

- **time_horizon** — Pick the closest match from: `intraday`, `days`, `weeks`,
  `months`, `long_term`, `unspecified`. If Kevin does not say, use `unspecified`.

- **rationale_quote** — A short verbatim or lightly paraphrased quote (20–80 words)
  from the transcript that best justifies the action you assigned. Include enough
  context to be meaningful on its own.

- **video_timestamp_seconds** — If the transcript includes segment timestamps (lines
  formatted as `[<N>s] <text>`), set this to the integer second where Kevin first
  makes the substantive statement about this ticker. If no timestamps are available,
  set to null.

## Rules for ticker inclusion

1. **Skip tickers mentioned only in passing.** Kevin often references tickers as
   examples or comparisons without making any recommendation. If he says fewer than
   ~20 words about a ticker with no clear directional signal, omit it from `tickers`.

2. **Do not duplicate tickers.** If Kevin mentions the same ticker multiple times,
   merge the signals into a single entry that represents his overall view from the
   video. Use the timestamp of the *first* substantive mention.

3. **Symbols only, no company names.** The `symbol` field must be a ticker, not a
   company name. "Nvidia" is wrong; "NVDA" is correct.

4. **Conviction scores are comparative.** Calibrate them relative to each other
   within the video — Kevin's "top conviction" pick in a video might be 0.85, while
   a hedged mention is 0.45.

## Quality checklist (review before calling submit_analysis)

- [ ] `market_outlook_direction` is one of: bullish, neutral, bearish, mixed
- [ ] `macro_themes` has 2–6 items, each a concise phrase
- [ ] `key_risks` has 2–5 items, each a concise phrase
- [ ] `summary` is approximately 200 words
- [ ] Every ticker in `tickers` has a clear actionable signal (no "I'm not sure")
- [ ] Tickers mentioned only in passing are omitted
- [ ] `conviction` values are floats in [0.0, 1.0]
- [ ] `time_horizon` is one of the six allowed values
- [ ] `rationale_quote` is grounded in something Kevin actually said
- [ ] You are calling `submit_analysis` exactly once with all required fields

Now read the transcript provided in the user message and call `submit_analysis`.
""".strip()

# ---------------------------------------------------------------------------
# Tool definition  (Anthropic tool-use format)
# ---------------------------------------------------------------------------

_ANALYSIS_TOOL: dict[str, Any] = {
    "name": "submit_analysis",
    "description": (
        "Submit the structured analysis of one Meet Kevin video. Call this exactly once."
    ),
    "input_schema": {
        "type": "object",
        "required": [
            "market_outlook_direction",
            "market_outlook_reasoning",
            "macro_themes",
            "key_risks",
            "summary",
            "tickers",
        ],
        "properties": {
            "market_outlook_direction": {
                "type": "string",
                "enum": ["bullish", "neutral", "bearish", "mixed"],
                "description": "Overall market sentiment direction",
            },
            "market_outlook_reasoning": {
                "type": "string",
                "description": "2-4 sentence explanation of the market outlook direction",
            },
            "macro_themes": {
                "type": "array",
                "items": {"type": "string"},
                "description": "2-6 high-level macro economic themes discussed",
            },
            "key_risks": {
                "type": "array",
                "items": {"type": "string"},
                "description": "2-5 principal downside risks Kevin mentions",
            },
            "summary": {
                "type": "string",
                "description": "~200-word plain-English investment thesis summary",
            },
            "tickers": {
                "type": "array",
                "description": "Per-ticker mentions with action and conviction",
                "items": {
                    "type": "object",
                    "required": [
                        "symbol",
                        "action",
                        "conviction",
                        "time_horizon",
                        "rationale_quote",
                        "video_timestamp_seconds",
                    ],
                    "properties": {
                        "symbol": {
                            "type": "string",
                            "description": "Uppercase ticker symbol (1-6 chars)",
                        },
                        "action": {
                            "type": "string",
                            "enum": ["buy", "sell", "hold", "watch", "avoid"],
                            "description": "Recommendation action",
                        },
                        "conviction": {
                            "type": "number",
                            "minimum": 0.0,
                            "maximum": 1.0,
                            "description": "Confidence in recommendation (0.0-1.0)",
                        },
                        "time_horizon": {
                            "type": "string",
                            "enum": [
                                "intraday",
                                "days",
                                "weeks",
                                "months",
                                "long_term",
                                "unspecified",
                            ],
                            "description": "Time horizon for the recommendation",
                        },
                        "rationale_quote": {
                            "type": "string",
                            "description": "Short verbatim or paraphrased quote from video",
                        },
                        "video_timestamp_seconds": {
                            "type": ["integer", "null"],
                            "description": "Timestamp in seconds for deep-link target",
                        },
                    },
                },
            },
        },
    },
}

# ---------------------------------------------------------------------------
# Public helpers
# ---------------------------------------------------------------------------


def compute_cost_usd(model: str, input_tokens: int, output_tokens: int) -> Decimal:
    """Compute LLM call cost in USD using pinned per-model pricing.

    Args:
        model: Model identifier string (must be a key in _PRICING).
        input_tokens: Number of input/prompt tokens consumed.
        output_tokens: Number of output/completion tokens generated.

    Returns:
        Cost as a Decimal. Returns Decimal("0") for unknown models (logs warning).
    """
    pricing = _PRICING.get(model)
    if pricing is None:
        logger.warning("compute_cost_usd: unknown model %r — returning zero cost", model)
        return Decimal("0")

    price_per_m_input, price_per_m_output = pricing
    million = Decimal("1000000")
    cost = (
        Decimal(input_tokens) / million * price_per_m_input
        + Decimal(output_tokens) / million * price_per_m_output
    )
    return cost.quantize(Decimal("0.0001"))


# ---------------------------------------------------------------------------
# Result dataclass
# ---------------------------------------------------------------------------


@dataclass(frozen=True)
class LlmCallResult:
    """Immutable result of one LLM analyze() call."""

    analysis: MeetKevinAnalysis
    raw_response: dict
    prompt_tokens: int
    completion_tokens: int
    cost_usd: Decimal


# ---------------------------------------------------------------------------
# Analyzer class
# ---------------------------------------------------------------------------

_MAX_SEGMENTS = 1000


class LlmAnalyzer:
    """Calls Claude (via native Anthropic SDK) to extract structured analysis from a video transcript.

    Args:
        client: Configured AsyncAnthropic client with OAuth bearer token.
        model: Model identifier (e.g. "claude-sonnet-4-5").
        prompt_version: Prompt version string stored in kevin_analyses.
    """

    def __init__(self, client: AsyncAnthropic, model: str, prompt_version: str) -> None:
        self._client = client
        self._model = model
        self._prompt_version = prompt_version

    async def analyze(
        self,
        *,
        title: str,
        description: str,
        published_at: datetime,
        transcript_text: str,
        transcript_segments: list[dict],
    ) -> LlmCallResult:
        """Run LLM analysis on a transcript and return a structured result.

        Args:
            title: Video title.
            description: Video description (may be empty).
            published_at: UTC publication timestamp.
            transcript_text: Full concatenated transcript text.
            transcript_segments: List of {start, end, text} dicts.

        Returns:
            LlmCallResult with parsed MeetKevinAnalysis and token accounting.

        Raises:
            ValueError: If the response contains no tool_use block.
            pydantic.ValidationError: If tool input fails schema validation.
        """
        user_msg = self._build_user_message(
            title=title,
            description=description,
            published_at=published_at,
            transcript_text=transcript_text,
            transcript_segments=transcript_segments,
        )

        response = await self._client.messages.create(
            model=self._model,
            max_tokens=4096,
            system=[
                {"type": "text", "text": SYSTEM_PROMPT, "cache_control": {"type": "ephemeral"}}
            ],
            tools=[_ANALYSIS_TOOL],
            tool_choice={"type": "tool", "name": "submit_analysis"},
            messages=[{"role": "user", "content": user_msg}],
        )

        # Find the first tool_use block in the response
        tool_use_block = None
        for block in response.content:
            if block.type == "tool_use":
                tool_use_block = block
                break

        if tool_use_block is None:
            raise ValueError(
                "LLM response contained no tool_use block (expected submit_analysis call)"
            )

        tool_input: dict = tool_use_block.input
        analysis = MeetKevinAnalysis.model_validate(tool_input)

        prompt_tokens: int = response.usage.input_tokens
        completion_tokens: int = response.usage.output_tokens
        cost_usd = compute_cost_usd(self._model, prompt_tokens, completion_tokens)

        raw_response: dict = {
            "stop_reason": response.stop_reason,
            "tool_name": tool_use_block.name,
            "tool_input": tool_input,
            "usage": {
                "input_tokens": prompt_tokens,
                "output_tokens": completion_tokens,
            },
        }

        return LlmCallResult(
            analysis=analysis,
            raw_response=raw_response,
            prompt_tokens=prompt_tokens,
            completion_tokens=completion_tokens,
            cost_usd=cost_usd,
        )

    # ------------------------------------------------------------------
    # Private helpers
    # ------------------------------------------------------------------

    def _build_user_message(
        self,
        *,
        title: str,
        description: str,
        published_at: datetime,
        transcript_text: str,
        transcript_segments: list[dict],
    ) -> str:
        """Build the user-turn message for the API call."""
        parts: list[str] = [
            f"Title: {title}",
            f"Published: {published_at.strftime('%Y-%m-%d %H:%M UTC')}",
        ]
        if description:
            parts.append(f"Description: {description}")

        parts.append("")  # blank line before transcript

        if transcript_segments:
            # Prefer timestamped segments (up to _MAX_SEGMENTS)
            segment_lines = [
                f"[{int(seg.get('start', 0))}s] {seg.get('text', '').strip()}"
                for seg in transcript_segments[:_MAX_SEGMENTS]
            ]
            parts.append("Transcript (with timestamps):")
            parts.extend(segment_lines)
        elif transcript_text:
            parts.append("Transcript:")
            parts.append(transcript_text)
        else:
            parts.append("Transcript: (no transcript available)")

        return "\n".join(parts)