initial commit — cassandra v0.1
Containerised macro-strategy dashboard: 4-panel web UI (indicators, portfolio, flash news, AI strategic log), MariaDB store, hourly ingestion jobs, OpenRouter-backed AI analysis. Ports the four prototype scripts in the parent dir (market_pulse, flash_news, trading212, strategic_log) into async services backed by a persistent DB and served via FastAPI + Jinja2 + HTMX. APScheduler runs as a separate compose service for crash-safety and easier restarts. Portfolio composition + position names come live from Trading 212; news per-ticker headlines reuse those names. Tone (NOVICE/INTERMEDIATE/ PRO) and analysis style (DRY/SPECULATIVE) are env-configurable and stored on each log row so historical entries show what produced them. Default model is deepseek/deepseek-v4-flash (overridable via env). Light/dark theme toggle, sans-serif for prose surfaces, monospace for data. Bearer-token auth, OpenRouter monthly cost cap, RSS feeds auto- disabled on consecutive failures. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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app/services/openrouter.py
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app/services/openrouter.py
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"""Strategic-log generator — DB-fed, OpenRouter-backed.
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Ported from /home/gg/ownCloud/Family/Finances/Wealth/strategic_log.py. The
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system prompt is preserved verbatim (the voice we converged on). The user
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prompt is now built from DB rows, not from subprocess JSON dumps.
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from datetime import datetime, timedelta, timezone
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import httpx
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from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential
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from app.config import get_settings
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OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
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# Bump when the composed prompt changes meaningfully. Stored on every
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# StrategicLog row so historical logs can be linked to the prompt that produced
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# them.
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PROMPT_VERSION = 3
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# --- Core: invariant across tone/analysis settings ----------------------------
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_CORE = """You are Cassandra, writing a single daily strategic markets log \
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for one specific investor. Synthesis, not exposition.
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# Lens
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- Geopolitics → markets is the primary causal chain. For each sector move, \
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ask: geopolitical, cyclical, or idiosyncratic. Label it.
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- Divergences and contradictions are where the information is. Hunt for them.
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- Absence of expected moves is signal. If the thesis predicted a reaction \
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that didn't happen, that's more interesting than the reactions that did.
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- Compare live readings against any reference snapshots provided.
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# Multi-source news
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- When state-aligned outlets (Xinhua, China Daily, RT) and Western outlets \
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cover the same event, read the gap in framing — that's the data.
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- News matters only insofar as it changes a market read. Color without \
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implications is filler.
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# Structure
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- One-line date header + any anchor framing (e.g. "Week 11 since Hormuz").
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- Immediately after the date header — with **nothing** in between — write a \
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TL;DR. Format it as:
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## TL;DR
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One concise paragraph of 2-3 sentences, **≤60 words total**, naming the \
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single most important read or divergence of the day with concrete numbers. \
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This is what a reader who only has 10 seconds sees. Don't waste it on the \
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weather or generic context.
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- Then 4-6 paragraphs, each anchored on a sleeve, sector, or theme. Concrete \
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numbers in every paragraph. No section over ~150 words.
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- One paragraph synthesising the news flow into a market read.
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- End with a watch list: 3-5 specific items to track in the next week, \
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each one sentence.
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# Discipline
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- No emojis, no marketing language, no "concerning" or "unprecedented" \
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without a specific number behind it.
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- Concrete > vague. "AMD +113% since the anchor" beats "AI stocks up sharply".
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- Distinguish "the thesis predicted X and X happened" from "the thesis \
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predicted X and X did not happen". Both are useful; conflating them is not.
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- Don't repeat the same point in different words across paragraphs.
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- No buy/sell recommendations. Triggers are pre-set elsewhere; your job is \
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to report whether reality is confirming, modifying, or refuting the thesis."""
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# --- Tone: audience-shaping block --------------------------------------------
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_TONE: dict[str, str] = {
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"NOVICE": """# Audience: novice
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The reader is new to markets. Define jargon the first time it appears (a \
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short clause in parentheses is fine). Avoid ticker shorthand without context. \
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Prefer everyday phrasing: "the price of US government debt fell, pushing \
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yields higher" rather than "the long end backed up". Keep paragraphs short. \
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Target ~600 words instead of ~800 so density stays digestible.""",
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"INTERMEDIATE": """# Audience: intermediate
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Assume the reader knows market basics (yield curves, breakevens, HY OAS, \
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sector ETFs). Use common terms without defining them, but stay clear of \
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deep institutional shorthand ("the belly", "duration trade", "carry pickup"). \
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Target ~700 words — lean and clear, no padding.""",
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"PRO": """# Audience: professional
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Assume institutional vocabulary. Use dense market shorthand freely. Don't \
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define standard terms. Target ~800 words. Density of insight > readability.""",
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}
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# --- Analysis: forward-vs-backward focus -------------------------------------
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_ANALYSIS: dict[str, str] = {
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"DRY": """# Analysis style: dry
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Report what happened. Identify divergences and contradictions. Compare to \
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references. Do not speculate on what comes next. Forward-looking statements \
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are limited to "what would invalidate the read" — never "we expect X to \
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happen". The watch list contains items to monitor, not predictions.""",
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"SPECULATIVE": """# Analysis style: speculative
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Report what happened, then explicitly explore forward scenarios. For each \
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significant sector or theme, sketch a 1-4 week scenario set: the base case \
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(what the data suggests), a contrarian case (what would invalidate it), and \
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what tape signal would tip you from one to the other. Be explicit about \
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uncertainty — say "the base case is" not "X will happen". The watch list is \
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the trip-wires that decide between scenarios.""",
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}
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def build_system_prompt(tone: str, analysis: str) -> str:
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"""Compose the system prompt from the chosen audience and analysis style."""
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tone_block = _TONE.get(tone.upper(), _TONE["INTERMEDIATE"])
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analysis_block = _ANALYSIS.get(analysis.upper(), _ANALYSIS["SPECULATIVE"])
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return "\n\n".join([_CORE, tone_block, analysis_block])
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# Backwards-compat: a default-composed SYSTEM_PROMPT for tests / callers that
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# don't yet pass tone/analysis. New callers should call build_system_prompt().
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SYSTEM_PROMPT = build_system_prompt("INTERMEDIATE", "SPECULATIVE")
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# --- Chat-mode overrides (sidebar on /log) -----------------------------------
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_CHAT_OVERRIDES = """# Chat mode (overrides the log-structure rules above)
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You are NOT writing a daily log right now. The user is asking a specific
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question via the chat sidebar.
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- Forget the date header, TL;DR, sectional structure, and watch list. Just answer.
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- Typical response: 200-400 words. Longer only if the question genuinely
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warrants it.
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- Cite specific numbers and named headlines from the reference materials
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below whenever relevant. If a number isn't in the context, don't invent it.
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- If a question is outside the provided context (e.g. asking about a stock or
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event not in the data), say so plainly rather than speculating from prior
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knowledge.
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- No buy/sell recommendations. If asked, redirect to thesis and scenarios.
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- Keep the same audience and analysis discipline established above."""
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def build_chat_system_prompt(
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tone: str,
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analysis: str,
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*,
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log_content: str | None,
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log_generated_at: datetime | None,
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quotes_by_group: dict[str, list[dict]],
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headlines: list[dict],
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reference_line: str | None = None,
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) -> str:
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"""Composed system prompt for the /log chat sidebar. Carries the user's
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chosen tone + analysis style and inlines the latest log + market data +
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headlines as reference material the model can cite from."""
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parts = [build_system_prompt(tone, analysis), "", _CHAT_OVERRIDES, ""]
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if reference_line:
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parts.append(f"# Doc reference snapshot\n{reference_line}\n")
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if log_content:
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ts = log_generated_at.strftime("%Y-%m-%d %H:%M UTC") if log_generated_at else "n/a"
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parts.append(f"# Latest strategic log (generated {ts})\n\n{log_content}\n")
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parts.append("# Live market data")
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parts.append(
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"```json\n" + json.dumps(quotes_by_group, indent=2, default=str)[:25000] + "\n```"
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)
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parts.append("# Recent headlines (last 24h, thesis-filtered top 50)")
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for h in headlines[:50]:
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parts.append(f"- [{h['source']}] {h['title']}")
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return "\n".join(parts)
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@dataclass
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class LogResult:
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content: str
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model: str
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prompt_tokens: int | None
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completion_tokens: int | None
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cost_usd: float | None
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def build_user_prompt(
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*,
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today: datetime,
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anchor: str | None,
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quotes_by_group: dict[str, list[dict]],
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headlines_by_bucket: dict[str, list[dict]],
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reference_line: str | None = None,
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) -> str:
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"""Assemble the user message from already-fetched-and-persisted data."""
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parts = [f"# Strategic log request — {today.strftime('%Y-%m-%d')}"]
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if anchor:
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parts.append(f"Anchor reference date: {anchor}")
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if reference_line:
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parts.append(
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"\n## Reference snapshot (when the macro thesis was authored)"
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f"\n{reference_line}\nCompare live readings against it."
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)
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parts.append("\n## Live market data (per group)")
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parts.append("```json\n" + json.dumps(quotes_by_group, indent=2, default=str) + "\n```")
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parts.append("\n## News flow (last 24h, filtered by bucket)")
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for label, items in headlines_by_bucket.items():
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if not items:
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continue
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parts.append(f"\n### {label.upper()}")
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for h in items[:30]:
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parts.append(f"- [{h['when'][:16].replace('T',' ')}] [{h['source']}] {h['title']}")
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parts.append(
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"\n## Task\nWrite the daily strategic log in ~800 words, following "
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"the discipline in the system prompt. No preamble; begin directly "
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"with the date header."
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)
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return "\n".join(parts)
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@retry(
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reraise=True,
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=2, min=2, max=30),
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retry=retry_if_exception_type((httpx.HTTPStatusError, httpx.TransportError)),
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)
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async def call_openrouter(
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client: httpx.AsyncClient,
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messages: list[dict],
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model: str,
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max_tokens: int = 4000,
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) -> LogResult:
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s = get_settings()
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if not s.OPENROUTER_API_KEY:
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raise RuntimeError("OPENROUTER_API_KEY not set")
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r = await client.post(
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OPENROUTER_URL,
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headers={
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"Authorization": f"Bearer {s.OPENROUTER_API_KEY}",
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"Content-Type": "application/json",
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"HTTP-Referer": "https://github.com/local/cassandra",
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"X-Title": "Cassandra",
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},
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json={"model": model, "messages": messages, "max_tokens": max_tokens},
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timeout=180,
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)
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r.raise_for_status()
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data = r.json()
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msg = data["choices"][0]["message"]
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# Some providers return null content + populated `reasoning` for thinking
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# models, or null content when finish_reason=length cut off the response.
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content = msg.get("content") or msg.get("reasoning")
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if not content:
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finish = data["choices"][0].get("finish_reason")
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raise RuntimeError(
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f"OpenRouter returned empty content (finish_reason={finish}, "
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f"model={model}, max_tokens={max_tokens})"
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)
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usage = data.get("usage") or {}
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return LogResult(
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content=content,
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model=model,
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prompt_tokens=usage.get("prompt_tokens"),
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completion_tokens=usage.get("completion_tokens"),
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cost_usd=usage.get("cost") or usage.get("total_cost"),
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)
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def month_window() -> tuple[datetime, datetime]:
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"""[start, now] in UTC for the current calendar month."""
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now = datetime.now(timezone.utc)
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start = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
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return start, now
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def month_start() -> datetime:
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return month_window()[0]
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