KOL feeds: fix dead/blocked sources, drop stale feeds (29→25)
Feed-health pass over KOL_FEEDS: - raoulpal: stale Substack (last 2024-05) → Real Vision podcast feed - dampedspring: paywalled (0 entries) → free "Damped Spring 101" Substack - unchained: Cloudflare 403 → canonical Megaphone podcast feed - lynalden: Cloudflare 202 → FeedBurner mirror - glassnode: recovered via httpx http2=True (was 403 on HTTP/1.1) - browser User-Agent + Accept headers on feed fetch - removed dead feeds with no active replacement: placeholder, dragonfly, niccarter, eugene - pin h2==4.3.0 (required by http2=True) All 25 remaining feeds verified fetching real body content; newest post per feed ≤88d. Bundles in-flight KOL-module work already in the working tree (kol_x ingest, migration 027, tests). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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"""Tests for x_analysis — the DETERMINISTIC normalization layer.
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The LLM call is mocked so each test feeds a controlled raw model response and
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asserts how analyze_x_post normalizes it. This locks down the enforcement rules
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that protect downstream consumers (kol_x → kol_divergence), independent of
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whatever the model actually returns:
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- retweet post_type → forced noise
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- trade_signal requires a buy/sell/reduce ticker with conviction ≥ 0.7,
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else it downgrades to directional (never silently dropped)
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- noise → tickers cleared + talks_vs_trades_flag forced false
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- ticker hygiene: conviction clamped 0..1, bad action → mention,
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overlong symbol dropped
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- invalid tier/post_type → safe defaults
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- bad JSON / empty text → graceful fallback, never raises
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These are pure logic (no network, no AI spend) so they're fast and stable.
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"""
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from __future__ import annotations
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import json
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import pytest
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from app.config import settings
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from app.services import x_analysis
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# ── helpers ────────────────────────────────────────────────────────────────
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def _patch_llm(monkeypatch, raw: dict):
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"""Force analyze_x_post's LLM call to return `raw` (serialized). We patch
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the OpenAI path (anthropic key blanked) since that's the default in CI."""
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payload = json.dumps(raw)
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class _Msg:
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content = payload
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class _Choice:
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message = _Msg()
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class _Resp:
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choices = [_Choice()]
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class _Completions:
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async def create(self, **_kw):
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return _Resp()
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class _Chat:
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completions = _Completions()
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class _Client:
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chat = _Chat()
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monkeypatch.setattr(settings, "anthropic_api_key", "") # → OpenAI path
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monkeypatch.setattr(x_analysis, "_oai", lambda: _Client())
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def _raw(**over):
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"""Minimal well-formed raw model response; override per test."""
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base = {
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"post_type": "original",
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"tier": "directional",
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"summary": "x",
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"tickers": [],
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"talks_vs_trades_flag": False,
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"has_price_target": False,
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"price_targets": [],
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"sentiment": "neutral",
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"reasoning": "y",
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}
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base.update(over)
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return base
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# ── tier enforcement ───────────────────────────────────────────────────────
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@pytest.mark.asyncio
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async def test_retweet_post_type_forced_to_noise(monkeypatch):
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_patch_llm(monkeypatch, _raw(
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post_type="retweet", tier="trade_signal",
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tickers=[{"ticker": "BTC", "action": "buy", "conviction": 0.9}],
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))
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r = await x_analysis.analyze_x_post(handle="k", text="some original-looking text body")
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assert r["tier"] == "noise"
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assert r["tickers"] == []
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@pytest.mark.asyncio
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async def test_trade_signal_downgrades_without_strong_action(monkeypatch):
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# tier=trade_signal but only a 'bullish' ticker (not buy/sell/reduce)
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_patch_llm(monkeypatch, _raw(
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tier="trade_signal",
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tickers=[{"ticker": "SOL", "action": "bullish", "conviction": 0.9}],
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))
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r = await x_analysis.analyze_x_post(handle="k", text="SOL looking strong")
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assert r["tier"] == "directional"
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assert r["tickers"][0]["ticker"] == "SOL"
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@pytest.mark.asyncio
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async def test_trade_signal_downgrades_on_low_conviction(monkeypatch):
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_patch_llm(monkeypatch, _raw(
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tier="trade_signal",
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tickers=[{"ticker": "SOL", "action": "buy", "conviction": 0.5}],
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))
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r = await x_analysis.analyze_x_post(handle="k", text="bought a little SOL maybe")
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assert r["tier"] == "directional" # 0.5 < 0.7 floor
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@pytest.mark.asyncio
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async def test_trade_signal_kept_when_strong(monkeypatch):
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_patch_llm(monkeypatch, _raw(
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tier="trade_signal",
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tickers=[{"ticker": "SOL", "action": "buy", "conviction": 0.9}],
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))
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r = await x_analysis.analyze_x_post(handle="k", text="aped SOL full size lfg")
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assert r["tier"] == "trade_signal"
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@pytest.mark.asyncio
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async def test_invalid_tier_falls_back_to_noise(monkeypatch):
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_patch_llm(monkeypatch, _raw(tier="超级买入"))
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r = await x_analysis.analyze_x_post(handle="k", text="ambiguous content here")
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assert r["tier"] == "noise"
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# ── noise enforcement ──────────────────────────────────────────────────────
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@pytest.mark.asyncio
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async def test_noise_clears_tickers_and_flag(monkeypatch):
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_patch_llm(monkeypatch, _raw(
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tier="noise", talks_vs_trades_flag=True,
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tickers=[{"ticker": "BTC", "action": "buy", "conviction": 0.9}],
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))
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r = await x_analysis.analyze_x_post(handle="k", text="gm frens beautiful day")
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assert r["tickers"] == []
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assert r["talks_vs_trades_flag"] is False
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# ── ticker hygiene ─────────────────────────────────────────────────────────
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@pytest.mark.asyncio
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async def test_conviction_clamped_to_unit_interval(monkeypatch):
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_patch_llm(monkeypatch, _raw(
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tier="directional",
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tickers=[{"ticker": "BTC", "action": "bullish", "conviction": 1.8}],
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))
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r = await x_analysis.analyze_x_post(handle="k", text="BTC going parabolic")
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assert r["tickers"][0]["conviction"] == 1.0
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@pytest.mark.asyncio
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async def test_invalid_action_becomes_mention(monkeypatch):
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_patch_llm(monkeypatch, _raw(
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tier="directional",
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tickers=[{"ticker": "BTC", "action": "yolo", "conviction": 0.5}],
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))
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r = await x_analysis.analyze_x_post(handle="k", text="BTC yolo time")
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assert r["tickers"][0]["action"] == "mention"
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@pytest.mark.asyncio
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async def test_overlong_ticker_dropped(monkeypatch):
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_patch_llm(monkeypatch, _raw(
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tier="directional",
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tickers=[{"ticker": "THISISWAYTOOLONG", "action": "bullish", "conviction": 0.5}],
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))
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r = await x_analysis.analyze_x_post(handle="k", text="some long token mention")
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assert r["tickers"] == []
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# ── graceful failure ───────────────────────────────────────────────────────
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@pytest.mark.asyncio
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async def test_empty_text_short_circuits_without_llm(monkeypatch):
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# no _patch_llm → if it called the LLM it would error; it must not.
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r = await x_analysis.analyze_x_post(handle="k", text=" ")
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assert r["tier"] == "noise"
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assert r["error"] == "empty post"
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@pytest.mark.asyncio
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async def test_bare_retweet_prefiltered_without_llm(monkeypatch):
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r = await x_analysis.analyze_x_post(handle="k", text="RT @someone: gm")
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assert r["tier"] == "noise"
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assert r["post_type"] == "retweet"
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@pytest.mark.asyncio
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async def test_bad_json_returns_fallback(monkeypatch):
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class _Msg:
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content = "not json at all {{{ "
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class _Choice:
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message = _Msg()
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class _Resp:
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choices = [_Choice()]
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class _Completions:
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async def create(self, **_kw):
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return _Resp()
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class _Chat:
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completions = _Completions()
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class _Client:
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chat = _Chat()
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monkeypatch.setattr(settings, "anthropic_api_key", "")
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monkeypatch.setattr(x_analysis, "_oai", lambda: _Client())
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r = await x_analysis.analyze_x_post(handle="k", text="real content that triggers llm")
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assert r["tier"] == "noise"
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assert r["error"] and "parse_error" in r["error"]
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