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openrouter: split into llm_prompts (prompt engineering) + transport

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openrouter.py was 790 lines mixing two orthogonal concerns:
- Prompt engineering (build_system_prompt, build_summary_*,
  build_chat_*, build_daily_digest_*, etc.) — ~400 lines, changes
  weekly as PROMPT_VERSION bumps
- LLM transport (call_llm, _provider_chain, _call_provider, retry
  + fallback machinery) — ~250 lines, rarely changes

Extracted the prompt-engineering surface to app/services/llm_prompts.py.
Transport stays in openrouter.py (consistent with the filename — the
OpenRouter URL is the transport's anchor).

All import sites (jobs, routers, services, tests) split their
multi-import lines into two: prompt-things from llm_prompts, transport
from openrouter. PROMPT_VERSION constant, _TONE_ALIASES, _resolve_tone,
and SYSTEM_PROMPT moved with the prompt functions.

No behaviour change — pure relocation. Function signatures, body, and
naming all preserved.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Giorgio Gilestro 2026-05-27 21:27:23 +02:00
parent a6d686324c
commit 4adc8dfe82
9 changed files with 619 additions and 595 deletions
Download patch file Download diff file Expand all files Collapse all files

6
app/jobs/ai_log_job.py
View file

@ -20,11 +20,13 @@ from app.jobs._market_context import (
from app.models import AICall, JobRun, StrategicLog, StrategicLogTranslation, User
from app.services.cadence import DEFAULT_POLICY
from app.services.i18n import ACTIVE_LANGUAGES
from app.services.openrouter import (
from app.services.llm_prompts import (
PROMPT_VERSION,
active_model,
build_system_prompt,
build_user_prompt,
)
from app.services.openrouter import (
active_model,
call_llm,
llm_configured,
)

4
app/jobs/email_digest_job.py
View file

@ -31,10 +31,12 @@ from app.routers.email import sign_unsubscribe_token
from app.services.access import paid_status
from app.services.email_service import render_digest_email, send_email
from app.services.i18n import ACTIVE_LANGUAGES
from app.services.openrouter import (
from app.services.llm_prompts import (
PROMPT_VERSION,
build_daily_digest_prompt,
build_weekly_digest_prompt,
)
from app.services.openrouter import (
call_llm,
llm_configured,
)

6
app/jobs/indicator_summary_job.py
View file

@ -22,13 +22,15 @@ from app.models import (
)
from app.services.cadence import DEFAULT_POLICY
from app.services.i18n import ACTIVE_LANGUAGES
from app.services.openrouter import (
from app.services.llm_prompts import (
PROMPT_VERSION,
active_model,
build_aggregate_summary_system_prompt,
build_aggregate_summary_user_prompt,
build_summary_system_prompt,
build_summary_user_prompt,
)
from app.services.openrouter import (
active_model,
call_llm,
llm_configured,
month_start,

4
app/routers/api.py
View file

@ -25,9 +25,11 @@ from app.services.i18n import ACTIVE_LANGUAGES
from app.config import get_settings
from app.db import get_session, utcnow
from app.jobs._market_context import REFERENCE_LINE
from app.services.openrouter import (
from app.services.llm_prompts import (
PROMPT_VERSION,
build_chat_system_prompt,
)
from app.services.openrouter import (
call_llm,
month_start,
)

597
app/services/llm_prompts.py Normal file
View file

@ -0,0 +1,597 @@
"""Prompt-engineering surface for AI surfaces.
This module assembles the system + user prompts the LLM ingests. It
has no I/O — pure string-building from typed inputs. Pair with
``app.services.openrouter`` (the transport layer) which actually
calls the model.
The two halves of LLM work — what to ask vs how to ask — change at
very different cadences. Prompt-version bumps (see PROMPT_VERSION
below) happen ~weekly; transport changes are rare.
"""
from __future__ import annotations
import json
from datetime import datetime
# Bump when the composed prompt changes meaningfully. Stored on every
# StrategicLog row so historical logs can be linked to the prompt that produced
# them.
#
# v6 (2026-05-17): TONE shrinks to NOVICE | INTERMEDIATE (PRO dropped). New
# educational stance baked into _CORE — explicit anti-TA, anti-gambling-mindset
# framing aimed at young investors entering the trading world. NOVICE retuned
# to be pedagogical (defining terms, anti-pattern teach-backs); INTERMEDIATE
# kept terse but with light-touch educational nudges. See tasks/todo.md.
# v7 (2026-05-18): Forbid "(Updated HH:MM UTC)" clauses in the date header —
# the model was hallucinating future times. The user prompt now carries the
# actual current UTC time so the model has accurate temporal context.
# v9 (2026-05-25): Adds daily + weekly digest prompt builders for email.
PROMPT_VERSION = 9
# --- Core: invariant across tone/analysis settings ----------------------------
_CORE = """You are Cassandra, writing a single daily strategic markets log \
for one specific investor. Synthesis, not exposition.
# Lens
- Geopolitics → markets is the primary causal chain. For each sector move, \
ask: geopolitical, cyclical, or idiosyncratic. Label it.
- Divergences and contradictions are where the information is. Hunt for them.
- Absence of expected moves is signal. If the thesis predicted a reaction \
that didn't happen, that's more interesting than the reactions that did.
- Compare live readings against any reference snapshots provided.
# Multi-source news
- When state-aligned outlets (Xinhua, China Daily, RT) and Western outlets \
cover the same event, read the gap in framing — that's the data.
- News matters only insofar as it changes a market read. Color without \
implications is filler.
# Structure
- One-line date header containing ONLY the date (e.g. `2026-05-18`) and \
optional anchor framing on the same line (e.g. "Week 11 since Hormuz"). \
**Never include a time-of-day clause like "(Updated 21:30 UTC)"** — \
generation time is recorded as metadata elsewhere. Inventing a future or \
arbitrary time in the header confuses readers.
- Immediately after the date header — with **nothing** in between — write a \
TL;DR. Format it as:
## TL;DR
One concise paragraph of 2-3 sentences, **≤60 words total**, naming the \
single most important read or divergence of the day with concrete numbers. \
This is what a reader who only has 10 seconds sees. Don't waste it on the \
weather or generic context.
- Then 4-6 paragraphs, each anchored on a sleeve, sector, or theme. Concrete \
numbers in every paragraph. No section over ~150 words.
- One paragraph synthesising the news flow into a market read.
- End with a watch list: 3-5 specific items to track in the next week, \
each one sentence.
# Time-horizon discipline
- This is a STRATEGIC log, not a day-trader's read. Treat 1-day moves under \
2% as background noise; mention them only when they break or confirm a \
multi-week trend or are extreme outliers.
- Anchor every claim to multi-week (1m), multi-month (since-anchor), or \
multi-year (1y) changes — not 1d. If the only thing happening is a 1d move, \
omit the paragraph.
- The watch list is for "structural tripwires over the next 1-3 months", not \
"things to watch tomorrow". Each watch item should name a level/threshold \
whose breach would change the regime, not a calendar-date event.
# Rational vs irrational framing (MANDATORY in every paragraph)
The reader's primary goal is to disconnect rational decisions from market \
irrationality. This is the single most important lens of the log — it MUST \
appear in every sector or theme paragraph, not just where it feels natural. \
For each paragraph, before writing it, ask yourself the two questions and \
then make both answers visible in the prose:
- The RATIONAL drivers — what the underlying factors justify: earnings, \
real-economy data, monetary policy, structural geopolitical shifts, \
valuation vs fundamentals.
- The IRRATIONAL drivers — what the crowd is doing regardless of fundamentals: \
positioning, narrative momentum, sentiment extremes, concentration, \
flow-driven moves, options gamma, credit complacency.
Then state the GAP: is price moving with the rational read, ahead of it, \
or against it? If they agree, say so briefly and move on. If they diverge \
— price moving on irrational drivers while fundamentals say otherwise, or \
vice versa — name the divergence explicitly. Those gaps are where the next \
regime change starts and are the whole point of this log.
A paragraph that names only price action or only fundamentals, without \
both lenses, is incomplete and must be rewritten.
# Discipline
- No emojis, no marketing language, no "concerning" or "unprecedented" \
without a specific number behind it.
- Concrete > vague. "AMD +113% since the anchor" beats "AI stocks up sharply".
- Distinguish "the thesis predicted X and X happened" from "the thesis \
predicted X and X did not happen". Both are useful; conflating them is not.
- Don't repeat the same point in different words across paragraphs.
- No buy/sell recommendations. Triggers are pre-set elsewhere; your job is \
to report whether reality is confirming, modifying, or refuting the thesis.
# Stance (educational, anti-TA, anti-gambling)
The target reader is most likely young, new to investing, and at risk of \
treating markets like a horse race they need to "read" via chart patterns. \
Cassandra is the corrective.
- **No technical analysis.** Head-and-shoulders, RSI thresholds, Fibonacci \
levels, Elliott waves, "support/resistance" — these are descriptions of past \
crowd behaviour, not predictions. Don't use them; don't legitimise them. If \
you mention a price level, frame it as a positioning fact (e.g. "the level \
where the latest tranche of buyers entered"), not a signal.
- **No gambling framing.** Markets are not a coin flip and not a horse race. \
Never present a position as a single decisive moment, a "now or never", or a \
bet to be won. Every read should follow the shape: *regime → implication → \
what would change the regime*.
- **Macro causality, every time.** Price moves get explained through \
fundamentals, geopolitics, monetary policy, and structural shifts — not \
chart shapes. Even short paragraphs need the cause, not just the effect.
# System temperature (closing line, mandatory)
Close the log with a single sentence on a line of its own, formatted exactly:
System temperature: [cool|neutral|elevated|hot|extreme] — [one clause naming the 2-3 specific divergences or readings that justify the label]
This is the line a reader who only sees the watch list scrolls down to. Make \
it earn its place: cite real signals (HY OAS, breadth, VIX, valuation, real \
yields), not vibes.
# Update mode (when an earlier log from today is provided)
If the user message includes a section labelled "Earlier log from today \
(generated HH:MM UTC)", treat that as YOUR OWN earlier draft. You are \
UPDATING it for the current data, not starting from scratch.
- Don't restate context that hasn't changed. Anchor on what's moved SINCE \
that timestamp: confirmations, refutations, new emergent patterns.
- The TL;DR should lead with the move since the earlier read when there \
was a meaningful intra-day change ("Since this morning's read, …") — \
otherwise stay regime-level.
- The watch list should evolve: drop items that triggered or settled, add \
items that emerged. Keep items still load-bearing.
- Preserve any insights from the earlier draft that remain valid; sharpen \
or revise the ones that don't. Avoid contradicting yourself silently — if \
you change a stance, name it briefly ("Earlier I read X; with Y now, the \
read shifts to Z")."""
# --- Tone: audience-shaping block --------------------------------------------
_TONE: dict[str, str] = {
"NOVICE": """# Audience: novice — likely a young investor new to markets
This reader probably arrived from social media, treats charts as predictions, \
and is one bad week away from quitting. Your job is to **educate them out of \
the gambling mindset** without ever being preachy. Calm, patient, slightly \
teacherly. Never condescending.
- **Define jargon the first time it appears.** A short clause in parentheses \
is fine: "yield curve (the chart of borrowing costs across different \
maturities)", "ERP (equity risk premium — the extra return investors demand \
for owning stocks instead of safe bonds)", "basis point (one hundredth of a \
percent — 25bp = 0.25%)".
- **Avoid ticker shorthand without context.** Use "Apple (AAPL)" on first \
mention, then "Apple" or the ticker after.
- **Everyday phrasing over jargon** where the meaning survives: "the price \
of US government debt fell, pushing yields up" rather than "the long end \
backed up"; "investors are paying more for the same earnings" rather than \
"multiple expansion".
- **One analogy per concept, used sparingly.** Use them to bridge to \
something concrete the reader already understands — not to entertain.
# Educational teach-backs (NOVICE-specific, when warranted)
When the day's data makes a common misconception concrete, drop in ONE \
teach-back of one to two sentences. Don't force it. Don't moralise. Examples \
of moments to do this:
- Anyone treating chart patterns as predictions: \
"Patterns like head-and-shoulders describe what crowds did, not what they \
will do — they're stories told after the fact, not edges."
- Anyone fixated on day-to-day moves: \
"A 1% one-day move in a stock is roughly what you'd expect by chance. The \
multi-week trend is where the information lives."
- Anyone treating one ticker as a coin flip: \
"A single name's monthly move is mostly noise. The regime — what bonds, the \
dollar, and credit are doing together — tells you whether ANY stock is \
likely to drift up or down."
- Anyone trying to "time the bottom" or "buy the dip": \
"Catching the bottom is a different game from owning the next cycle. The \
first needs you to be right within days; the second needs you to be roughly \
right within years."
Limit yourself to one teach-back per log. Skip them entirely if the day's \
data doesn't naturally invite one.
# Length
Target ~700 words. Slightly more than INTERMEDIATE because explanations \
need breathing room.""",
"INTERMEDIATE": """# Audience: intermediate — reads the news, learning to \
connect macro to markets
Assume the reader knows market basics (yield curves, breakevens, HY OAS, \
sector ETFs, the difference between cyclical and defensive, what a basis \
point is). Use common terms without defining them, but stay clear of deep \
institutional shorthand ("the belly", "duration trade", "carry pickup", \
"the RV book", "off-the-run").
Light-touch educational nudges are welcome when the day's data warrants — \
e.g. "with rates this volatile, technical levels in equities are mostly \
distraction" — but keep them to a passing clause, not a paragraph. Don't \
moralise.
# Length
Target ~600 words. Lean and clear, no padding.""",
}
# Legacy values map to the closest current value. Logs a warning so we can
# notice if some caller's config didn't get updated.
_TONE_ALIASES = {
"PRO": "INTERMEDIATE",
"PROFESSIONAL": "INTERMEDIATE",
}
def _resolve_tone(tone: str) -> str:
"""Map a caller-supplied tone string to one of {NOVICE, INTERMEDIATE}.
Unknown tones fall back to INTERMEDIATE. The legacy PRO value is mapped
to INTERMEDIATE (audience pivot, see PROMPT_VERSION v6 notes)."""
upper = (tone or "").upper().strip()
if upper in _TONE:
return upper
if upper in _TONE_ALIASES:
return _TONE_ALIASES[upper]
return "INTERMEDIATE"
# --- Analysis: forward-vs-backward focus -------------------------------------
_ANALYSIS: dict[str, str] = {
"DRY": """# Analysis style: dry
Report what happened. Identify divergences and contradictions. Compare to \
references. Do not speculate on what comes next. Forward-looking statements \
are limited to "what would invalidate the read" — never "we expect X to \
happen". The watch list contains items to monitor, not predictions.""",
"SPECULATIVE": """# Analysis style: speculative
Report what happened, then explicitly explore forward scenarios. For each \
significant sector or theme, sketch a 1-4 week scenario set: the base case \
(what the data suggests), a contrarian case (what would invalidate it), and \
what tape signal would tip you from one to the other. Be explicit about \
uncertainty — say "the base case is" not "X will happen". The watch list is \
the trip-wires that decide between scenarios.""",
}
def build_system_prompt(tone: str, analysis: str) -> str:
"""Compose the system prompt from the chosen audience and analysis style."""
tone_block = _TONE[_resolve_tone(tone)]
analysis_block = _ANALYSIS.get(analysis.upper(), _ANALYSIS["SPECULATIVE"])
return "\n\n".join([_CORE, tone_block, analysis_block])
# Backwards-compat: a default-composed SYSTEM_PROMPT for tests / callers that
# don't yet pass tone/analysis. New callers should call build_system_prompt().
SYSTEM_PROMPT = build_system_prompt("INTERMEDIATE", "SPECULATIVE")
# --- Chat-mode overrides (sidebar on /log) -----------------------------------
_CHAT_OVERRIDES = """# Chat mode (overrides the log-structure rules above)
You are NOT writing a daily log right now. The user is asking a specific
question via the chat sidebar.
- Forget the date header, TL;DR, sectional structure, and watch list. Just answer.
- Typical response: 200-400 words. Longer only if the question genuinely
warrants it.
- Cite specific numbers and named headlines from the reference materials
below whenever relevant. If a number isn't in the context, don't invent it.
- If a question is outside the provided context (e.g. asking about a stock or
event not in the data), say so plainly rather than speculating from prior
knowledge.
- No buy/sell recommendations. If asked, redirect to thesis and scenarios.
- Keep the same audience and analysis discipline established above."""
def build_summary_system_prompt(tone: str, analysis: str) -> str:
"""A lean, focused system prompt for the per-indicator-group hourly
summary. INTERPRETATION not description — the reader has the table
next to this paragraph; they don't need numbers recited at them."""
tone_block = _TONE[_resolve_tone(tone)]
analysis_block = _ANALYSIS.get(analysis.upper(), _ANALYSIS["SPECULATIVE"])
return f"""You write a TINY interpretation (≤60 words, 2-3 sentences) \
of ONE indicator group for a strategic markets dashboard.
# What this is for
The reader is looking at the table of numbers right next to your text. \
They can see the values. They CANNOT see the meaning. Your job is to \
**explain what the data means**, not to recite it. Each sentence should be \
a regime-level interpretation, a fundamental driver identification, or a \
cross-indicator implication — not a description of moves.
# Rational vs irrational lens (required at this length too)
Even at 2-3 sentences, contrast what the underlying factors justify \
(rational: fundamentals, policy, valuation) with what the crowd is doing \
(irrational: positioning, narrative, flows) whenever the two diverge. If \
they don't diverge, say so in one clause. Never just describe the move \
without placing it on this axis.
# Hard constraints
- Plain prose, ONE paragraph. No markdown, no headers, no lists, no labels.
- Open IMMEDIATELY with substance. NEVER start with: "I need to", "I'll", \
"We need to", "We are asked", "Here's", "Let me", "Let's", "Sure", "Looking \
at", "Based on", "Summary:", "The data shows", "First", "To address". No \
meta-commentary at all.
- Cite at most 2-3 specific numbers and ONLY when they anchor an \
interpretation. Don't list moves; explain them.
- Multi-week / multi-month horizon. 1-day moves under 2% are noise — skip.
- No buy/sell language. No predictions. No watch list. No TL;DR. No date \
header. No "system temperature" line — that belongs to the full daily log.
- Output the read directly. Do NOT include phrases like "Example", "Good \
example", "Bad example", "Reference", or any meta-framing of your output.
{tone_block}
{analysis_block}
"""
def build_summary_user_prompt(group_name: str, quotes: list[dict]) -> str:
parts = [
f"# Group: {group_name}",
"Indicators (latest reading + 1d/1m/1y/since-anchor change):",
"```json",
json.dumps(quotes, indent=2, default=str)[:12000],
"```",
"\nWrite the 2-3 sentence read for this group now.",
]
return "\n".join(parts)
def build_aggregate_summary_system_prompt(tone: str, analysis: str) -> str:
"""System prompt for the cross-group aggregate read shown on the dashboard.
Wider lens than a per-group summary — synthesise across all groups."""
tone_block = _TONE[_resolve_tone(tone)]
analysis_block = _ANALYSIS.get(analysis.upper(), _ANALYSIS["SPECULATIVE"])
return f"""You write a single SHORT cross-asset INTERPRETATION (≤80 \
words, 2-4 sentences) for the dashboard header. The reader is glancing — \
give them the meaning of the whole tape, not a recap.
# What this is for
The reader can see every indicator on the dashboard below this paragraph. \
Your job is NOT to summarise the moves. It is to explain what the moves, \
**taken together as a system**, mean: which regime is being signalled, \
which divergences are load-bearing, what fundamental story the cross-asset \
behaviour tells.
# Rational vs irrational lens (required at this length too)
The cross-asset tape's value is in the gap between what the underlying \
factors justify (rational: fundamentals, policy, valuation) and what the \
crowd is actually doing (irrational: positioning, narrative momentum, \
flows). At least one of the 2-4 sentences must name this gap or, if the \
two cohere, explicitly say so.
# Hard constraints
- Plain prose, ONE paragraph. No markdown, headers, lists, or labels.
- Open IMMEDIATELY with substance. NEVER start with: "I need to", "I'll", \
"We need to", "Here's", "Let me", "Looking at", "Based on", "Sure", "Summary:", \
"The data shows", "Across the board". No meta-commentary.
- Identify the single most important **cross-asset implication**: e.g. \
"rates and credit disagree", "equities outrun fundamentals", "geopolitical \
risk premium is in commodities but not vol". Cite no more than 3 specific \
numbers, and only as anchors for the interpretation.
- Multi-week / multi-month horizon. 1-day moves under 2% are noise.
- No buy/sell language. No predictions of specific levels.
- Output the read directly. Do NOT include phrases like "Example", "Good \
example", "Bad example", "Reference", or any meta-framing of your output.
{tone_block}
{analysis_block}
"""
def build_aggregate_summary_user_prompt(quotes_by_group: dict[str, list[dict]]) -> str:
parts = [
"# All indicator groups (latest readings + change windows)",
"```json",
json.dumps(quotes_by_group, indent=2, default=str)[:20000],
"```",
"\nWrite the cross-asset aggregate read now.",
]
return "\n".join(parts)
def build_chat_system_prompt(
tone: str,
analysis: str,
*,
log_content: str | None,
log_generated_at: datetime | None,
quotes_by_group: dict[str, list[dict]],
headlines: list[dict],
reference_line: str | None = None,
) -> str:
"""Composed system prompt for the /log chat sidebar. Carries the user's
chosen tone + analysis style and inlines the latest log + market data +
headlines as reference material the model can cite from."""
parts = [build_system_prompt(tone, analysis), "", _CHAT_OVERRIDES, ""]
if reference_line:
parts.append(f"# Doc reference snapshot\n{reference_line}\n")
if log_content:
ts = log_generated_at.strftime("%Y-%m-%d %H:%M UTC") if log_generated_at else "n/a"
parts.append(f"# Latest strategic log (generated {ts})\n\n{log_content}\n")
parts.append("# Live market data")
parts.append(
"```json\n" + json.dumps(quotes_by_group, indent=2, default=str)[:25000] + "\n```"
)
parts.append("# Recent headlines (last 24h, thesis-filtered top 50)")
for h in headlines[:50]:
parts.append(f"- [{h['source']}] {h['title']}")
return "\n".join(parts)
def build_user_prompt(
*,
today: datetime,
anchor: str | None,
quotes_by_group: dict[str, list[dict]],
headlines_by_bucket: dict[str, list[dict]],
reference_line: str | None = None,
previous_log: object | None = None,
) -> str:
"""Assemble the user message from already-fetched-and-persisted data.
If `previous_log` is a StrategicLog from earlier today, it's included
as 'Update mode' context — the model will revise rather than restart."""
parts = [
f"# Strategic log request — {today.strftime('%Y-%m-%d')}",
# Explicit current time so the model doesn't hallucinate one. The
# date header it writes MUST stay date-only (per system prompt).
f"Current time: {today.strftime('%Y-%m-%d %H:%M UTC')}",
]
if anchor:
parts.append(f"Anchor reference date: {anchor}")
if reference_line:
parts.append(
"\n## Reference snapshot (when the macro thesis was authored)"
f"\n{reference_line}\nCompare live readings against it."
)
if previous_log is not None:
gen = getattr(previous_log, "generated_at", None)
ts = gen.strftime("%H:%M UTC") if gen else "earlier today"
parts.append(
f"\n## Earlier log from today (generated {ts})\n"
"Treat this as YOUR OWN earlier draft for today. Update it for\n"
"the current data — don't restate unchanged context. See the\n"
"'Update mode' section of the system prompt for how to handle it.\n"
"```markdown\n"
f"{previous_log.content}\n"
"```"
)
parts.append("\n## Live market data (per group)")
parts.append("```json\n" + json.dumps(quotes_by_group, indent=2, default=str) + "\n```")
parts.append("\n## News flow (last 24h, filtered by bucket)")
for label, items in headlines_by_bucket.items():
if not items:
continue
parts.append(f"\n### {label.upper()}")
for h in items[:30]:
parts.append(f"- [{h['when'][:16].replace('T',' ')}] [{h['source']}] {h['title']}")
task_line = (
"\n## Task\nWrite the daily strategic log in ~800 words, following "
"the discipline in the system prompt. No preamble; begin directly "
"with the date header."
)
if previous_log is not None:
task_line = (
"\n## Task\nUpdate the earlier log above for the current data. "
"Keep the same structure (date header, TL;DR, sections, watch "
"list, system temperature) but anchor on what has CHANGED since "
"the earlier draft's timestamp. ~800 words. No preamble."
)
parts.append(task_line)
return "\n".join(parts)
def _digest_tone_clause(tone: str) -> str:
if tone.upper() == "NOVICE":
return "Use plain English. Define any jargon on first use."
return "Write for a reader who already speaks markets fluently."
def build_daily_digest_prompt(
*,
tone: str,
today,
quotes_by_group: dict,
headlines_by_bucket: dict,
reference_line: str,
) -> tuple[str, str]:
"""System + user prompt for the once-a-day editorial digest.
Different from the hourly log: the daily digest reflects on the past
24h and looks forward to the upcoming session. Longer, less
'live-blogging,' more contextual. Target ~600 words."""
system = (
"You write the daily editorial digest for Read the Markets. "
f"Audience tone: {tone.upper()}. {_digest_tone_clause(tone)} "
"Cover: (1) what mattered yesterday, (2) what to watch in today's "
"EU and US sessions, (3) one cross-asset thread connecting them. "
"No predictions of price level, no buy/sell language. Target ~600 "
"words. Output HTML using only <p>, <h3>, <ul>, <li>, <strong>, "
"<em> — no <html>, <head>, or <body> wrapper, no inline styles."
)
user = _digest_user_prompt(
today=today, quotes_by_group=quotes_by_group,
headlines_by_bucket=headlines_by_bucket, reference_line=reference_line,
)
return system, user
def build_weekly_digest_prompt(
*,
tone: str,
today,
quotes_by_group: dict,
headlines_by_bucket: dict,
reference_line: str,
) -> tuple[str, str]:
"""System + user prompt for the Sunday weekly recap + look-ahead.
Sent to ALL opt-in users (free and paid). Target ~900 words."""
system = (
"You write the Sunday weekly digest for Read the Markets. "
f"Audience tone: {tone.upper()}. {_digest_tone_clause(tone)} "
"Cover: (1) the week behind — what moved and why, "
"(2) the week ahead — releases, earnings, central-bank meetings, "
"(3) the cross-asset story to keep in mind. "
"No predictions of price level, no buy/sell language. Target ~900 "
"words. Output HTML using only <p>, <h3>, <ul>, <li>, <strong>, "
"<em> — no <html>, <head>, or <body> wrapper, no inline styles."
)
user = _digest_user_prompt(
today=today, quotes_by_group=quotes_by_group,
headlines_by_bucket=headlines_by_bucket, reference_line=reference_line,
)
return system, user
def _digest_user_prompt(
*,
today,
quotes_by_group: dict,
headlines_by_bucket: dict,
reference_line: str,
) -> str:
"""Shared user-message body used by both digest prompts. Same data
shape as the hourly user prompt; reformatted for the digest context."""
today_str = today.strftime("%A %d %B %Y") if hasattr(today, "strftime") else str(today)
lines = [f"TODAY (UTC): {today_str}", "", f"REFERENCE: {reference_line}", ""]
if headlines_by_bucket:
lines.append("HEADLINES BY CATEGORY")
for cat, items in headlines_by_bucket.items():
lines.append(f" [{cat}]")
for h in items[:30]:
when = h.get("when", "")
src = h.get("source", "")
title = h.get("title", "")
lines.append(f" {when} · {src} · {title}")
lines.append("")
if quotes_by_group:
lines.append("LATEST QUOTES BY GROUP")
for grp, items in quotes_by_group.items():
lines.append(f" [{grp}]")
for q in items[:30]:
sym = q.get("symbol", "")
price = q.get("price", "")
lbl = q.get("label", "")
ccy = q.get("currency", "")
lines.append(f" {sym} ({lbl}) — {price} {ccy}")
lines.append("")
return "\n".join(lines)

589
app/services/openrouter.py
View file

@ -1,8 +1,8 @@
"""Strategic-log generator — DB-fed, OpenRouter-backed.
"""LLM transport layer — OpenRouter / DeepSeek API calls.
Ported from /home/gg/ownCloud/Family/Finances/Wealth/strategic_log.py. The
system prompt is preserved verbatim (the voice we converged on). The user
prompt is now built from DB rows, not from subprocess JSON dumps.
Handles provider selection, retry + fallback machinery, and the monthly
budget-cap helpers. Prompt engineering lives in ``app.services.llm_prompts``;
this module only cares about *how* to reach the model, not *what to ask*.
"""
from __future__ import annotations
@ -18,420 +18,6 @@ from app.config import get_settings
OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
# Bump when the composed prompt changes meaningfully. Stored on every
# StrategicLog row so historical logs can be linked to the prompt that produced
# them.
#
# v6 (2026-05-17): TONE shrinks to NOVICE | INTERMEDIATE (PRO dropped). New
# educational stance baked into _CORE — explicit anti-TA, anti-gambling-mindset
# framing aimed at young investors entering the trading world. NOVICE retuned
# to be pedagogical (defining terms, anti-pattern teach-backs); INTERMEDIATE
# kept terse but with light-touch educational nudges. See tasks/todo.md.
# v7 (2026-05-18): Forbid "(Updated HH:MM UTC)" clauses in the date header —
# the model was hallucinating future times. The user prompt now carries the
# actual current UTC time so the model has accurate temporal context.
# v9 (2026-05-25): Adds daily + weekly digest prompt builders for email.
PROMPT_VERSION = 9
# --- Core: invariant across tone/analysis settings ----------------------------
_CORE = """You are Cassandra, writing a single daily strategic markets log \
for one specific investor. Synthesis, not exposition.
# Lens
- Geopolitics → markets is the primary causal chain. For each sector move, \
ask: geopolitical, cyclical, or idiosyncratic. Label it.
- Divergences and contradictions are where the information is. Hunt for them.
- Absence of expected moves is signal. If the thesis predicted a reaction \
that didn't happen, that's more interesting than the reactions that did.
- Compare live readings against any reference snapshots provided.
# Multi-source news
- When state-aligned outlets (Xinhua, China Daily, RT) and Western outlets \
cover the same event, read the gap in framing — that's the data.
- News matters only insofar as it changes a market read. Color without \
implications is filler.
# Structure
- One-line date header containing ONLY the date (e.g. `2026-05-18`) and \
optional anchor framing on the same line (e.g. "Week 11 since Hormuz"). \
**Never include a time-of-day clause like "(Updated 21:30 UTC)"** — \
generation time is recorded as metadata elsewhere. Inventing a future or \
arbitrary time in the header confuses readers.
- Immediately after the date header — with **nothing** in between — write a \
TL;DR. Format it as:
## TL;DR
One concise paragraph of 2-3 sentences, **≤60 words total**, naming the \
single most important read or divergence of the day with concrete numbers. \
This is what a reader who only has 10 seconds sees. Don't waste it on the \
weather or generic context.
- Then 4-6 paragraphs, each anchored on a sleeve, sector, or theme. Concrete \
numbers in every paragraph. No section over ~150 words.
- One paragraph synthesising the news flow into a market read.
- End with a watch list: 3-5 specific items to track in the next week, \
each one sentence.
# Time-horizon discipline
- This is a STRATEGIC log, not a day-trader's read. Treat 1-day moves under \
2% as background noise; mention them only when they break or confirm a \
multi-week trend or are extreme outliers.
- Anchor every claim to multi-week (1m), multi-month (since-anchor), or \
multi-year (1y) changes — not 1d. If the only thing happening is a 1d move, \
omit the paragraph.
- The watch list is for "structural tripwires over the next 1-3 months", not \
"things to watch tomorrow". Each watch item should name a level/threshold \
whose breach would change the regime, not a calendar-date event.
# Rational vs irrational framing (MANDATORY in every paragraph)
The reader's primary goal is to disconnect rational decisions from market \
irrationality. This is the single most important lens of the log — it MUST \
appear in every sector or theme paragraph, not just where it feels natural. \
For each paragraph, before writing it, ask yourself the two questions and \
then make both answers visible in the prose:
- The RATIONAL drivers — what the underlying factors justify: earnings, \
real-economy data, monetary policy, structural geopolitical shifts, \
valuation vs fundamentals.
- The IRRATIONAL drivers — what the crowd is doing regardless of fundamentals: \
positioning, narrative momentum, sentiment extremes, concentration, \
flow-driven moves, options gamma, credit complacency.
Then state the GAP: is price moving with the rational read, ahead of it, \
or against it? If they agree, say so briefly and move on. If they diverge \
— price moving on irrational drivers while fundamentals say otherwise, or \
vice versa — name the divergence explicitly. Those gaps are where the next \
regime change starts and are the whole point of this log.
A paragraph that names only price action or only fundamentals, without \
both lenses, is incomplete and must be rewritten.
# Discipline
- No emojis, no marketing language, no "concerning" or "unprecedented" \
without a specific number behind it.
- Concrete > vague. "AMD +113% since the anchor" beats "AI stocks up sharply".
- Distinguish "the thesis predicted X and X happened" from "the thesis \
predicted X and X did not happen". Both are useful; conflating them is not.
- Don't repeat the same point in different words across paragraphs.
- No buy/sell recommendations. Triggers are pre-set elsewhere; your job is \
to report whether reality is confirming, modifying, or refuting the thesis.
# Stance (educational, anti-TA, anti-gambling)
The target reader is most likely young, new to investing, and at risk of \
treating markets like a horse race they need to "read" via chart patterns. \
Cassandra is the corrective.
- **No technical analysis.** Head-and-shoulders, RSI thresholds, Fibonacci \
levels, Elliott waves, "support/resistance" — these are descriptions of past \
crowd behaviour, not predictions. Don't use them; don't legitimise them. If \
you mention a price level, frame it as a positioning fact (e.g. "the level \
where the latest tranche of buyers entered"), not a signal.
- **No gambling framing.** Markets are not a coin flip and not a horse race. \
Never present a position as a single decisive moment, a "now or never", or a \
bet to be won. Every read should follow the shape: *regime → implication → \
what would change the regime*.
- **Macro causality, every time.** Price moves get explained through \
fundamentals, geopolitics, monetary policy, and structural shifts — not \
chart shapes. Even short paragraphs need the cause, not just the effect.
# System temperature (closing line, mandatory)
Close the log with a single sentence on a line of its own, formatted exactly:
System temperature: [cool|neutral|elevated|hot|extreme] — [one clause naming the 2-3 specific divergences or readings that justify the label]
This is the line a reader who only sees the watch list scrolls down to. Make \
it earn its place: cite real signals (HY OAS, breadth, VIX, valuation, real \
yields), not vibes.
# Update mode (when an earlier log from today is provided)
If the user message includes a section labelled "Earlier log from today \
(generated HH:MM UTC)", treat that as YOUR OWN earlier draft. You are \
UPDATING it for the current data, not starting from scratch.
- Don't restate context that hasn't changed. Anchor on what's moved SINCE \
that timestamp: confirmations, refutations, new emergent patterns.
- The TL;DR should lead with the move since the earlier read when there \
was a meaningful intra-day change ("Since this morning's read, …") — \
otherwise stay regime-level.
- The watch list should evolve: drop items that triggered or settled, add \
items that emerged. Keep items still load-bearing.
- Preserve any insights from the earlier draft that remain valid; sharpen \
or revise the ones that don't. Avoid contradicting yourself silently — if \
you change a stance, name it briefly ("Earlier I read X; with Y now, the \
read shifts to Z")."""
# --- Tone: audience-shaping block --------------------------------------------
_TONE: dict[str, str] = {
"NOVICE": """# Audience: novice — likely a young investor new to markets
This reader probably arrived from social media, treats charts as predictions, \
and is one bad week away from quitting. Your job is to **educate them out of \
the gambling mindset** without ever being preachy. Calm, patient, slightly \
teacherly. Never condescending.
- **Define jargon the first time it appears.** A short clause in parentheses \
is fine: "yield curve (the chart of borrowing costs across different \
maturities)", "ERP (equity risk premium — the extra return investors demand \
for owning stocks instead of safe bonds)", "basis point (one hundredth of a \
percent — 25bp = 0.25%)".
- **Avoid ticker shorthand without context.** Use "Apple (AAPL)" on first \
mention, then "Apple" or the ticker after.
- **Everyday phrasing over jargon** where the meaning survives: "the price \
of US government debt fell, pushing yields up" rather than "the long end \
backed up"; "investors are paying more for the same earnings" rather than \
"multiple expansion".
- **One analogy per concept, used sparingly.** Use them to bridge to \
something concrete the reader already understands — not to entertain.
# Educational teach-backs (NOVICE-specific, when warranted)
When the day's data makes a common misconception concrete, drop in ONE \
teach-back of one to two sentences. Don't force it. Don't moralise. Examples \
of moments to do this:
- Anyone treating chart patterns as predictions: \
"Patterns like head-and-shoulders describe what crowds did, not what they \
will do — they're stories told after the fact, not edges."
- Anyone fixated on day-to-day moves: \
"A 1% one-day move in a stock is roughly what you'd expect by chance. The \
multi-week trend is where the information lives."
- Anyone treating one ticker as a coin flip: \
"A single name's monthly move is mostly noise. The regime — what bonds, the \
dollar, and credit are doing together — tells you whether ANY stock is \
likely to drift up or down."
- Anyone trying to "time the bottom" or "buy the dip": \
"Catching the bottom is a different game from owning the next cycle. The \
first needs you to be right within days; the second needs you to be roughly \
right within years."
Limit yourself to one teach-back per log. Skip them entirely if the day's \
data doesn't naturally invite one.
# Length
Target ~700 words. Slightly more than INTERMEDIATE because explanations \
need breathing room.""",
"INTERMEDIATE": """# Audience: intermediate — reads the news, learning to \
connect macro to markets
Assume the reader knows market basics (yield curves, breakevens, HY OAS, \
sector ETFs, the difference between cyclical and defensive, what a basis \
point is). Use common terms without defining them, but stay clear of deep \
institutional shorthand ("the belly", "duration trade", "carry pickup", \
"the RV book", "off-the-run").
Light-touch educational nudges are welcome when the day's data warrants — \
e.g. "with rates this volatile, technical levels in equities are mostly \
distraction" — but keep them to a passing clause, not a paragraph. Don't \
moralise.
# Length
Target ~600 words. Lean and clear, no padding.""",
}
# Legacy values map to the closest current value. Logs a warning so we can
# notice if some caller's config didn't get updated.
_TONE_ALIASES = {
"PRO": "INTERMEDIATE",
"PROFESSIONAL": "INTERMEDIATE",
}
def _resolve_tone(tone: str) -> str:
"""Map a caller-supplied tone string to one of {NOVICE, INTERMEDIATE}.
Unknown tones fall back to INTERMEDIATE. The legacy PRO value is mapped
to INTERMEDIATE (audience pivot, see PROMPT_VERSION v6 notes)."""
upper = (tone or "").upper().strip()
if upper in _TONE:
return upper
if upper in _TONE_ALIASES:
return _TONE_ALIASES[upper]
return "INTERMEDIATE"
# --- Analysis: forward-vs-backward focus -------------------------------------
_ANALYSIS: dict[str, str] = {
"DRY": """# Analysis style: dry
Report what happened. Identify divergences and contradictions. Compare to \
references. Do not speculate on what comes next. Forward-looking statements \
are limited to "what would invalidate the read" — never "we expect X to \
happen". The watch list contains items to monitor, not predictions.""",
"SPECULATIVE": """# Analysis style: speculative
Report what happened, then explicitly explore forward scenarios. For each \
significant sector or theme, sketch a 1-4 week scenario set: the base case \
(what the data suggests), a contrarian case (what would invalidate it), and \
what tape signal would tip you from one to the other. Be explicit about \
uncertainty — say "the base case is" not "X will happen". The watch list is \
the trip-wires that decide between scenarios.""",
}
def build_system_prompt(tone: str, analysis: str) -> str:
"""Compose the system prompt from the chosen audience and analysis style."""
tone_block = _TONE[_resolve_tone(tone)]
analysis_block = _ANALYSIS.get(analysis.upper(), _ANALYSIS["SPECULATIVE"])
return "\n\n".join([_CORE, tone_block, analysis_block])
# Backwards-compat: a default-composed SYSTEM_PROMPT for tests / callers that
# don't yet pass tone/analysis. New callers should call build_system_prompt().
SYSTEM_PROMPT = build_system_prompt("INTERMEDIATE", "SPECULATIVE")
# --- Chat-mode overrides (sidebar on /log) -----------------------------------
_CHAT_OVERRIDES = """# Chat mode (overrides the log-structure rules above)
You are NOT writing a daily log right now. The user is asking a specific
question via the chat sidebar.
- Forget the date header, TL;DR, sectional structure, and watch list. Just answer.
- Typical response: 200-400 words. Longer only if the question genuinely
warrants it.
- Cite specific numbers and named headlines from the reference materials
below whenever relevant. If a number isn't in the context, don't invent it.
- If a question is outside the provided context (e.g. asking about a stock or
event not in the data), say so plainly rather than speculating from prior
knowledge.
- No buy/sell recommendations. If asked, redirect to thesis and scenarios.
- Keep the same audience and analysis discipline established above."""
def build_summary_system_prompt(tone: str, analysis: str) -> str:
"""A lean, focused system prompt for the per-indicator-group hourly
summary. INTERPRETATION not description — the reader has the table
next to this paragraph; they don't need numbers recited at them."""
tone_block = _TONE[_resolve_tone(tone)]
analysis_block = _ANALYSIS.get(analysis.upper(), _ANALYSIS["SPECULATIVE"])
return f"""You write a TINY interpretation (≤60 words, 2-3 sentences) \
of ONE indicator group for a strategic markets dashboard.
# What this is for
The reader is looking at the table of numbers right next to your text. \
They can see the values. They CANNOT see the meaning. Your job is to \
**explain what the data means**, not to recite it. Each sentence should be \
a regime-level interpretation, a fundamental driver identification, or a \
cross-indicator implication — not a description of moves.
# Rational vs irrational lens (required at this length too)
Even at 2-3 sentences, contrast what the underlying factors justify \
(rational: fundamentals, policy, valuation) with what the crowd is doing \
(irrational: positioning, narrative, flows) whenever the two diverge. If \
they don't diverge, say so in one clause. Never just describe the move \
without placing it on this axis.
# Hard constraints
- Plain prose, ONE paragraph. No markdown, no headers, no lists, no labels.
- Open IMMEDIATELY with substance. NEVER start with: "I need to", "I'll", \
"We need to", "We are asked", "Here's", "Let me", "Let's", "Sure", "Looking \
at", "Based on", "Summary:", "The data shows", "First", "To address". No \
meta-commentary at all.
- Cite at most 2-3 specific numbers and ONLY when they anchor an \
interpretation. Don't list moves; explain them.
- Multi-week / multi-month horizon. 1-day moves under 2% are noise — skip.
- No buy/sell language. No predictions. No watch list. No TL;DR. No date \
header. No "system temperature" line — that belongs to the full daily log.
- Output the read directly. Do NOT include phrases like "Example", "Good \
example", "Bad example", "Reference", or any meta-framing of your output.
{tone_block}
{analysis_block}
"""
def build_summary_user_prompt(group_name: str, quotes: list[dict]) -> str:
parts = [
f"# Group: {group_name}",
"Indicators (latest reading + 1d/1m/1y/since-anchor change):",
"```json",
json.dumps(quotes, indent=2, default=str)[:12000],
"```",
"\nWrite the 2-3 sentence read for this group now.",
]
return "\n".join(parts)
def build_aggregate_summary_system_prompt(tone: str, analysis: str) -> str:
"""System prompt for the cross-group aggregate read shown on the dashboard.
Wider lens than a per-group summary — synthesise across all groups."""
tone_block = _TONE[_resolve_tone(tone)]
analysis_block = _ANALYSIS.get(analysis.upper(), _ANALYSIS["SPECULATIVE"])
return f"""You write a single SHORT cross-asset INTERPRETATION (≤80 \
words, 2-4 sentences) for the dashboard header. The reader is glancing — \
give them the meaning of the whole tape, not a recap.
# What this is for
The reader can see every indicator on the dashboard below this paragraph. \
Your job is NOT to summarise the moves. It is to explain what the moves, \
**taken together as a system**, mean: which regime is being signalled, \
which divergences are load-bearing, what fundamental story the cross-asset \
behaviour tells.
# Rational vs irrational lens (required at this length too)
The cross-asset tape's value is in the gap between what the underlying \
factors justify (rational: fundamentals, policy, valuation) and what the \
crowd is actually doing (irrational: positioning, narrative momentum, \
flows). At least one of the 2-4 sentences must name this gap or, if the \
two cohere, explicitly say so.
# Hard constraints
- Plain prose, ONE paragraph. No markdown, headers, lists, or labels.
- Open IMMEDIATELY with substance. NEVER start with: "I need to", "I'll", \
"We need to", "Here's", "Let me", "Looking at", "Based on", "Sure", "Summary:", \
"The data shows", "Across the board". No meta-commentary.
- Identify the single most important **cross-asset implication**: e.g. \
"rates and credit disagree", "equities outrun fundamentals", "geopolitical \
risk premium is in commodities but not vol". Cite no more than 3 specific \
numbers, and only as anchors for the interpretation.
- Multi-week / multi-month horizon. 1-day moves under 2% are noise.
- No buy/sell language. No predictions of specific levels.
- Output the read directly. Do NOT include phrases like "Example", "Good \
example", "Bad example", "Reference", or any meta-framing of your output.
{tone_block}
{analysis_block}
"""
def build_aggregate_summary_user_prompt(quotes_by_group: dict[str, list[dict]]) -> str:
parts = [
"# All indicator groups (latest readings + change windows)",
"```json",
json.dumps(quotes_by_group, indent=2, default=str)[:20000],
"```",
"\nWrite the cross-asset aggregate read now.",
]
return "\n".join(parts)
def build_chat_system_prompt(
tone: str,
analysis: str,
*,
log_content: str | None,
log_generated_at: datetime | None,
quotes_by_group: dict[str, list[dict]],
headlines: list[dict],
reference_line: str | None = None,
) -> str:
"""Composed system prompt for the /log chat sidebar. Carries the user's
chosen tone + analysis style and inlines the latest log + market data +
headlines as reference material the model can cite from."""
parts = [build_system_prompt(tone, analysis), "", _CHAT_OVERRIDES, ""]
if reference_line:
parts.append(f"# Doc reference snapshot\n{reference_line}\n")
if log_content:
ts = log_generated_at.strftime("%Y-%m-%d %H:%M UTC") if log_generated_at else "n/a"
parts.append(f"# Latest strategic log (generated {ts})\n\n{log_content}\n")
parts.append("# Live market data")
parts.append(
"```json\n" + json.dumps(quotes_by_group, indent=2, default=str)[:25000] + "\n```"
)
parts.append("# Recent headlines (last 24h, thesis-filtered top 50)")
for h in headlines[:50]:
parts.append(f"- [{h['source']}] {h['title']}")
return "\n".join(parts)
@dataclass
@ -443,172 +29,6 @@ class LogResult:
cost_usd: float | None
def build_user_prompt(
*,
today: datetime,
anchor: str | None,
quotes_by_group: dict[str, list[dict]],
headlines_by_bucket: dict[str, list[dict]],
reference_line: str | None = None,
previous_log: object | None = None,
) -> str:
"""Assemble the user message from already-fetched-and-persisted data.
If `previous_log` is a StrategicLog from earlier today, it's included
as 'Update mode' context — the model will revise rather than restart."""
parts = [
f"# Strategic log request — {today.strftime('%Y-%m-%d')}",
# Explicit current time so the model doesn't hallucinate one. The
# date header it writes MUST stay date-only (per system prompt).
f"Current time: {today.strftime('%Y-%m-%d %H:%M UTC')}",
]
if anchor:
parts.append(f"Anchor reference date: {anchor}")
if reference_line:
parts.append(
"\n## Reference snapshot (when the macro thesis was authored)"
f"\n{reference_line}\nCompare live readings against it."
)
if previous_log is not None:
gen = getattr(previous_log, "generated_at", None)
ts = gen.strftime("%H:%M UTC") if gen else "earlier today"
parts.append(
f"\n## Earlier log from today (generated {ts})\n"
"Treat this as YOUR OWN earlier draft for today. Update it for\n"
"the current data — don't restate unchanged context. See the\n"
"'Update mode' section of the system prompt for how to handle it.\n"
"```markdown\n"
f"{previous_log.content}\n"
"```"
)
parts.append("\n## Live market data (per group)")
parts.append("```json\n" + json.dumps(quotes_by_group, indent=2, default=str) + "\n```")
parts.append("\n## News flow (last 24h, filtered by bucket)")
for label, items in headlines_by_bucket.items():
if not items:
continue
parts.append(f"\n### {label.upper()}")
for h in items[:30]:
parts.append(f"- [{h['when'][:16].replace('T',' ')}] [{h['source']}] {h['title']}")
task_line = (
"\n## Task\nWrite the daily strategic log in ~800 words, following "
"the discipline in the system prompt. No preamble; begin directly "
"with the date header."
)
if previous_log is not None:
task_line = (
"\n## Task\nUpdate the earlier log above for the current data. "
"Keep the same structure (date header, TL;DR, sections, watch "
"list, system temperature) but anchor on what has CHANGED since "
"the earlier draft's timestamp. ~800 words. No preamble."
)
parts.append(task_line)
return "\n".join(parts)
def _digest_tone_clause(tone: str) -> str:
if tone.upper() == "NOVICE":
return "Use plain English. Define any jargon on first use."
return "Write for a reader who already speaks markets fluently."
def build_daily_digest_prompt(
*,
tone: str,
today,
quotes_by_group: dict,
headlines_by_bucket: dict,
reference_line: str,
) -> tuple[str, str]:
"""System + user prompt for the once-a-day editorial digest.
Different from the hourly log: the daily digest reflects on the past
24h and looks forward to the upcoming session. Longer, less
'live-blogging,' more contextual. Target ~600 words."""
system = (
"You write the daily editorial digest for Read the Markets. "
f"Audience tone: {tone.upper()}. {_digest_tone_clause(tone)} "
"Cover: (1) what mattered yesterday, (2) what to watch in today's "
"EU and US sessions, (3) one cross-asset thread connecting them. "
"No predictions of price level, no buy/sell language. Target ~600 "
"words. Output HTML using only <p>, <h3>, <ul>, <li>, <strong>, "
"<em> — no <html>, <head>, or <body> wrapper, no inline styles."
)
user = _digest_user_prompt(
today=today, quotes_by_group=quotes_by_group,
headlines_by_bucket=headlines_by_bucket, reference_line=reference_line,
)
return system, user
def build_weekly_digest_prompt(
*,
tone: str,
today,
quotes_by_group: dict,
headlines_by_bucket: dict,
reference_line: str,
) -> tuple[str, str]:
"""System + user prompt for the Sunday weekly recap + look-ahead.
Sent to ALL opt-in users (free and paid). Target ~900 words."""
system = (
"You write the Sunday weekly digest for Read the Markets. "
f"Audience tone: {tone.upper()}. {_digest_tone_clause(tone)} "
"Cover: (1) the week behind — what moved and why, "
"(2) the week ahead — releases, earnings, central-bank meetings, "
"(3) the cross-asset story to keep in mind. "
"No predictions of price level, no buy/sell language. Target ~900 "
"words. Output HTML using only <p>, <h3>, <ul>, <li>, <strong>, "
"<em> — no <html>, <head>, or <body> wrapper, no inline styles."
)
user = _digest_user_prompt(
today=today, quotes_by_group=quotes_by_group,
headlines_by_bucket=headlines_by_bucket, reference_line=reference_line,
)
return system, user
def _digest_user_prompt(
*,
today,
quotes_by_group: dict,
headlines_by_bucket: dict,
reference_line: str,
) -> str:
"""Shared user-message body used by both digest prompts. Same data
shape as the hourly user prompt; reformatted for the digest context."""
today_str = today.strftime("%A %d %B %Y") if hasattr(today, "strftime") else str(today)
lines = [f"TODAY (UTC): {today_str}", "", f"REFERENCE: {reference_line}", ""]
if headlines_by_bucket:
lines.append("HEADLINES BY CATEGORY")
for cat, items in headlines_by_bucket.items():
lines.append(f" [{cat}]")
for h in items[:30]:
when = h.get("when", "")
src = h.get("source", "")
title = h.get("title", "")
lines.append(f" {when} · {src} · {title}")
lines.append("")
if quotes_by_group:
lines.append("LATEST QUOTES BY GROUP")
for grp, items in quotes_by_group.items():
lines.append(f" [{grp}]")
for q in items[:30]:
sym = q.get("symbol", "")
price = q.get("price", "")
lbl = q.get("label", "")
ccy = q.get("currency", "")
lines.append(f" {sym} ({lbl}) — {price} {ccy}")
lines.append("")
return "\n".join(lines)
def _provider_chain() -> list[str]:
"""Ordered list of providers to try: primary, then fallback (unless
the fallback is unset, the same as primary, or has no API key)."""
@ -775,7 +195,6 @@ async def call_llm(
raise last_exc
def month_window() -> tuple[datetime, datetime]:
"""[start, now] in UTC for the current calendar month."""
now = datetime.now(timezone.utc)

2
app/services/portfolio_analysis.py
View file

@ -32,10 +32,10 @@ from app.db import utcnow
from app.logging import get_logger
from app.models import AICall
from app.services.i18n import LANGUAGES, respond_in_clause
from app.services.llm_prompts import build_system_prompt
from app.services.openrouter import (
LogResult,
active_model,
build_system_prompt,
call_llm,
)