Giorgio Gilestro
a10409c02b
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>
118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
"""Runtime configuration — environment via Pydantic Settings + TOML-loaded data tables.
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Settings come from .env / process env. The TOML files (default.toml, portfolio.toml)
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define *what to track* — they're declarative content, not config knobs, so they
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stay separate from the settings model.
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"""
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from __future__ import annotations
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import tomllib
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from functools import lru_cache
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from pathlib import Path
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from typing import Any
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from pydantic import Field
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from pydantic_settings import BaseSettings, SettingsConfigDict
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CONFIG_DIR = Path(__file__).resolve().parent.parent / "config"
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class Settings(BaseSettings):
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"""All runtime knobs. Read from process env, .env not needed in-container
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because compose injects vars directly; .env is supported for local dev."""
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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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extra="ignore",
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)
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# Database
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DATABASE_URL: str = "mysql+aiomysql://cassandra:changeme@db:3306/cassandra"
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# API keys (mirror prototype .env names)
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API_KEY: str = "" # Trading 212 key
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SECRET_KEY: str = "" # Trading 212 secret
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FRED_API_KEY: str = ""
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OPENROUTER_API_KEY: str = ""
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# App
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CASSANDRA_TOKEN: str = ""
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CASSANDRA_PORT: int = 8000
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CASSANDRA_BASE_CURRENCY: str = "GBP"
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CASSANDRA_ANCHOR_DATE: str = ""
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CASSANDRA_MOCK: bool = False
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# AI log
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OPENROUTER_MODEL: str = "deepseek/deepseek-v4-flash"
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OPENROUTER_MONTHLY_CAP_USD: float = 20.0
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CASSANDRA_TONE: str = "INTERMEDIATE" # NOVICE | INTERMEDIATE | PRO
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CASSANDRA_ANALYSIS: str = "SPECULATIVE" # DRY | SPECULATIVE
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# Config file locations (overridable for tests)
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BASELINE_TOML: Path = Field(default_factory=lambda: CONFIG_DIR / "default.toml")
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PORTFOLIO_TOML: Path = Field(default_factory=lambda: CONFIG_DIR / "portfolio.toml")
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@lru_cache
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def get_settings() -> Settings:
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return Settings()
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# --- TOML data tables --------------------------------------------------------
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def _merge_toml(*paths: Path) -> dict[str, Any]:
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"""Read TOML files in order; later ones override earlier at the top level
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(with shallow dict-merge for nested tables)."""
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out: dict[str, Any] = {}
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for path in paths:
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if not path.exists():
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continue
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with path.open("rb") as f:
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data = tomllib.load(f)
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for k, v in data.items():
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if isinstance(v, dict) and isinstance(out.get(k), dict):
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out[k].update(v)
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else:
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out[k] = v
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return out
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def load_groups(*paths: Path) -> dict[str, list[tuple[str, str, str]]]:
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"""[(symbol, label, note), ...] per group name."""
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data = _merge_toml(*paths)
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out: dict[str, list[tuple[str, str, str]]] = {}
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for name, items in (data.get("groups") or {}).items():
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out[name] = [
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(it["symbol"], it.get("label", it["symbol"]), it.get("note", ""))
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for it in items
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]
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return out
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def load_feeds(*paths: Path) -> dict[str, list[tuple[str, str]]]:
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"""[(name, url), ...] per category name."""
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data = _merge_toml(*paths)
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out: dict[str, list[tuple[str, str]]] = {}
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for cat, items in (data.get("feeds") or {}).items():
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out[cat] = [(it["name"], it["url"]) for it in items]
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return out
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def load_presets(*paths: Path) -> dict[str, list[str]]:
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"""Keyword presets for news filtering."""
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data = _merge_toml(*paths)
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presets = (data.get("news") or {}).get("presets") or {}
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return {name: list(kw) for name, kw in presets.items()}
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def load_all() -> tuple[dict, dict, dict]:
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"""Shortcut: groups, feeds, presets using the configured TOML paths."""
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s = get_settings()
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return (
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load_groups(s.BASELINE_TOML, s.PORTFOLIO_TOML),
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load_feeds(s.BASELINE_TOML, s.PORTFOLIO_TOML),
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load_presets(s.BASELINE_TOML, s.PORTFOLIO_TOML),
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)
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