feat(V2): migrazione macro completa (read-only, env ignored)
- exchanges/macro: cot.py + cot_contracts.py + fetchers.py copiati 1:1 con rewrite import mcp_common -> cerbero_mcp.common, mcp_macro -> cerbero_mcp.exchanges.macro - nuovo MacroClient stateless wrapper: trasporta solo fred_api_key/finnhub_api_key, niente HTTP session (i fetchers usano async_client ad-hoc) - tools.py: 11 tool (get_treasury_yields, get_yield_curve_slope, get_breakeven_inflation, get_economic_indicators, get_macro_calendar, get_market_overview, get_equity_futures, get_asset_price, get_cot_tff, get_cot_disaggregated, get_cot_extreme_positioning) — niente write, niente leverage_cap - routers/macro.py: prefix /mcp-macro, 11 route POST /tools/* - builder branch macro: stesse credenziali per testnet/mainnet (env ignorato); registry istanzia 2 entry, costo trascurabile (wrapper stateless) - test migrati: test_cot.py + test_fetchers.py (test_server_acl.py skippato V1-only) - nuovo test test_build_client_macro_no_env_distinction in test_exchanges_builder.py Suite: 224 passed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
AdrianoDev
@@ -44,4 +44,14 @@ async def build_client(
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secret_key=settings.alpaca.secret_key.get_secret_value(),
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paper=(env == "testnet"),
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)
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if exchange == "macro":
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# Read-only data provider — env ignored. Il registry
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# istanzia comunque 2 entry (testnet/mainnet); costo trascurabile
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# (wrapper stateless senza HTTP session).
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from cerbero_mcp.exchanges.macro.client import MacroClient
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return MacroClient(
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fred_api_key=settings.macro.fred_api_key.get_secret_value(),
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finnhub_api_key=settings.macro.finnhub_api_key.get_secret_value(),
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)
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raise ValueError(f"unsupported exchange: {exchange}")
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@@ -0,0 +1,20 @@
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"""MacroClient: contenitore credenziali per data provider macro (FRED, Finnhub).
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Macro è un read-only data provider: nessun testnet/mainnet, nessuna sessione
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HTTP persistente — i `fetchers.py` aprono client httpx ad-hoc tramite
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`cerbero_mcp.common.http.async_client`. Questo wrapper esiste solo per uniformità
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con il pattern degli altri exchange (DeribitClient/BybitClient/...) e per essere
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istanziato dal `ClientRegistry`.
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"""
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from __future__ import annotations
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class MacroClient:
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"""Wrapper credenziali FRED/Finnhub. Stateless, no HTTP session."""
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def __init__(self, *, fred_api_key: str = "", finnhub_api_key: str = "") -> None:
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self.fred_api_key = fred_api_key
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self.finnhub_api_key = finnhub_api_key
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async def aclose(self) -> None: # pragma: no cover - no-op, no resources
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return None
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@@ -0,0 +1,91 @@
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"""Pure-logic helpers per COT report parsing e analytics.
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Niente HTTP qui — orchestrazione fetch sta in fetchers.py. Tutto testabile
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in isolamento.
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"""
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from __future__ import annotations
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from typing import Literal
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ExtremeSignal = Literal["extreme_short", "extreme_long", "neutral"]
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def compute_percentile(value: float, history: list[float]) -> float | None:
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"""Percentile di `value` rispetto ad `history` (0-100, inclusive).
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Restituisce None se history vuoto. Clipped a [0, 100] se value fuori range.
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"""
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if not history:
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return None
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n = len(history)
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below_or_eq = sum(1 for h in history if h <= value)
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pct = 100.0 * below_or_eq / n
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return max(0.0, min(100.0, pct))
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def classify_extreme(percentile: float | None, threshold: float = 5.0) -> ExtremeSignal:
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"""Classifica un percentile come estremo short/long o neutral.
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threshold default 5 → flagga ≤ 5 come short, ≥ 100-5=95 come long.
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"""
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if percentile is None:
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return "neutral"
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if percentile <= threshold:
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return "extreme_short"
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if percentile >= 100.0 - threshold:
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return "extreme_long"
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return "neutral"
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def _to_int(v) -> int:
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try:
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return int(float(v))
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except (TypeError, ValueError):
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return 0
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def _date_only(s: str) -> str:
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"""Estrae 'YYYY-MM-DD' da una data ISO con o senza timestamp."""
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if not s:
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return ""
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return s.split("T", 1)[0]
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def parse_tff_row(raw: dict) -> dict:
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"""Mappa una row Socrata TFF al formato API output."""
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dl = _to_int(raw.get("dealer_positions_long_all"))
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ds = _to_int(raw.get("dealer_positions_short_all"))
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al = _to_int(raw.get("asset_mgr_positions_long"))
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as_ = _to_int(raw.get("asset_mgr_positions_short"))
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ll = _to_int(raw.get("lev_money_positions_long"))
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ls = _to_int(raw.get("lev_money_positions_short"))
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ol = _to_int(raw.get("other_rept_positions_long"))
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os_ = _to_int(raw.get("other_rept_positions_short"))
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return {
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"report_date": _date_only(raw.get("report_date_as_yyyy_mm_dd", "")),
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"dealer_long": dl, "dealer_short": ds, "dealer_net": dl - ds,
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"asset_mgr_long": al, "asset_mgr_short": as_, "asset_mgr_net": al - as_,
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"lev_funds_long": ll, "lev_funds_short": ls, "lev_funds_net": ll - ls,
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"other_long": ol, "other_short": os_, "other_net": ol - os_,
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"open_interest": _to_int(raw.get("open_interest_all")),
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}
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def parse_disagg_row(raw: dict) -> dict:
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"""Mappa una row Socrata Disaggregated F&O combined al formato API output."""
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pl = _to_int(raw.get("prod_merc_positions_long_all"))
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ps = _to_int(raw.get("prod_merc_positions_short_all"))
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sl = _to_int(raw.get("swap_positions_long_all"))
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ss = _to_int(raw.get("swap_positions_short_all"))
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ml = _to_int(raw.get("m_money_positions_long_all"))
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ms = _to_int(raw.get("m_money_positions_short_all"))
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ol = _to_int(raw.get("other_rept_positions_long_all"))
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os_ = _to_int(raw.get("other_rept_positions_short_all"))
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return {
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"report_date": _date_only(raw.get("report_date_as_yyyy_mm_dd", "")),
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"producer_long": pl, "producer_short": ps, "producer_net": pl - ps,
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"swap_long": sl, "swap_short": ss, "swap_net": sl - ss,
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"managed_money_long": ml, "managed_money_short": ms, "managed_money_net": ml - ms,
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"other_long": ol, "other_short": os_, "other_net": ol - os_,
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"open_interest": _to_int(raw.get("open_interest_all")),
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}
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@@ -0,0 +1,36 @@
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"""Costanti CFTC: ticker → contract_market_code per TFF e Disaggregated.
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I codici CFTC (`cftc_contract_market_code`) sono pubblici e stabili nel tempo.
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Riferimento: https://www.cftc.gov/MarketReports/CommitmentsofTraders/
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"""
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from __future__ import annotations
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CFTC_BASE_URL = "https://publicreporting.cftc.gov/resource"
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TFF_DATASET_ID = "gpe5-46if"
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DISAGG_DATASET_ID = "72hh-3qpy"
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# TFF: equity/financial. Mapping ticker → cftc_contract_market_code.
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SYMBOL_TO_CFTC_CODE_TFF: dict[str, str] = {
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"ES": "13874A", # E-mini S&P 500
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"NQ": "209742", # E-mini Nasdaq-100
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"RTY": "239742", # E-mini Russell 2000
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"ZN": "043602", # 10-Year T-Note
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"ZB": "020601", # 30-Year T-Bond
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"6E": "099741", # Euro FX
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"6J": "097741", # Japanese Yen
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"DX": "098662", # US Dollar Index
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}
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# Disaggregated: commodities.
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SYMBOL_TO_CFTC_CODE_DISAGG: dict[str, str] = {
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"CL": "067651", # Crude Oil WTI
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"GC": "088691", # Gold
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"SI": "084691", # Silver
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"HG": "085692", # Copper
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"ZW": "001602", # Wheat
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"ZC": "002602", # Corn
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"ZS": "005602", # Soybeans
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}
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ALL_TFF_SYMBOLS: list[str] = list(SYMBOL_TO_CFTC_CODE_TFF.keys())
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ALL_DISAGG_SYMBOLS: list[str] = list(SYMBOL_TO_CFTC_CODE_DISAGG.keys())
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@@ -0,0 +1,771 @@
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from __future__ import annotations
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from datetime import UTC, datetime, timedelta
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from typing import Any
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import httpx
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from cerbero_mcp.common.http import async_client
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from cerbero_mcp.exchanges.macro.cot import classify_extreme, compute_percentile, parse_disagg_row, parse_tff_row
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from cerbero_mcp.exchanges.macro.cot_contracts import (
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ALL_DISAGG_SYMBOLS,
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ALL_TFF_SYMBOLS,
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CFTC_BASE_URL,
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DISAGG_DATASET_ID,
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SYMBOL_TO_CFTC_CODE_DISAGG,
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SYMBOL_TO_CFTC_CODE_TFF,
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TFF_DATASET_ID,
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)
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FRED_BASE = "https://api.stlouisfed.org/fred/series/observations"
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FINNHUB_CALENDAR = "https://finnhub.io/api/v1/calendar/economic"
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COINGECKO_GLOBAL = "https://api.coingecko.com/api/v3/global"
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COINGECKO_SIMPLE = "https://api.coingecko.com/api/v3/simple/price"
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DERIBIT_DVOL = "https://www.deribit.com/api/v2/public/get_volatility_index_data"
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YAHOO_CHART = "https://query1.finance.yahoo.com/v8/finance/chart/{symbol}"
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ASSET_TICKER_MAP: dict[str, tuple[str, str]] = {
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"WTI": ("CL=F", "WTI Crude Oil"),
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"BRENT": ("BZ=F", "Brent Crude Oil"),
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"GOLD": ("GC=F", "Gold Futures"),
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"SILVER": ("SI=F", "Silver Futures"),
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"COPPER": ("HG=F", "Copper Futures"),
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"NATGAS": ("NG=F", "Natural Gas"),
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"DXY": ("DX-Y.NYB", "US Dollar Index"),
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"SPX": ("^GSPC", "S&P 500"),
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"NDX": ("^NDX", "Nasdaq 100"),
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"DJI": ("^DJI", "Dow Jones"),
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"RUT": ("^RUT", "Russell 2000"),
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"VIX": ("^VIX", "CBOE Volatility Index"),
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"US5Y": ("^FVX", "US 5-Year Treasury"),
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"US10Y": ("^TNX", "US 10-Year Treasury"),
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"US30Y": ("^TYX", "US 30-Year Treasury"),
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"US2Y": ("^UST2YR", "US 2-Year Treasury"),
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"EURUSD": ("EURUSD=X", "EUR/USD"),
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"USDJPY": ("JPY=X", "USD/JPY"),
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"GBPUSD": ("GBPUSD=X", "GBP/USD"),
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"BTCUSD": ("BTC-USD", "Bitcoin/USD"),
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"ETHUSD": ("ETH-USD", "Ethereum/USD"),
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"ES": ("ES=F", "E-mini S&P 500 Futures"),
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"NQ": ("NQ=F", "E-mini Nasdaq 100 Futures"),
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"YM": ("YM=F", "E-mini Dow Futures"),
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"RTY": ("RTY=F", "E-mini Russell 2000 Futures"),
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}
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_ASSET_CACHE: dict[str, dict] = {}
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_ASSET_CACHE_TTL = 60.0
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async def _fetch_yahoo_meta(client: httpx.AsyncClient, symbol: str, range_: str = "10d") -> dict:
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try:
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resp = await client.get(
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YAHOO_CHART.format(symbol=symbol),
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params={"interval": "1d", "range": range_},
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headers={"User-Agent": "Mozilla/5.0"},
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)
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if resp.status_code != 200:
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return {}
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result = (resp.json().get("chart") or {}).get("result") or []
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if not result:
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return {}
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r0 = result[0]
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meta = r0.get("meta") or {}
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closes = ((r0.get("indicators") or {}).get("quote") or [{}])[0].get("close") or []
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closes = [c for c in closes if c is not None]
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return {"meta": meta, "closes": closes}
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except Exception:
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return {}
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async def fetch_asset_price(ticker: str) -> dict[str, Any]:
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import time
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key = ticker.upper()
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now = time.monotonic()
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cached = _ASSET_CACHE.get(key)
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if cached and (now - cached["ts"]) < _ASSET_CACHE_TTL:
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return cached["data"]
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mapping = ASSET_TICKER_MAP.get(key)
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if not mapping:
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return {"ticker": ticker, "error": f"unknown ticker {ticker}"}
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symbol, name = mapping
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async with async_client(timeout=10.0) as client:
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info = await _fetch_yahoo_meta(client, symbol, "10d")
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meta = info.get("meta") or {}
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closes = info.get("closes") or []
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price = meta.get("regularMarketPrice")
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prev_close = meta.get("previousClose")
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change_24h_pct = None
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if price is not None and prev_close:
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try:
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change_24h_pct = round((float(price) - float(prev_close)) / float(prev_close) * 100, 3)
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except Exception:
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change_24h_pct = None
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change_7d_pct = None
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if len(closes) >= 6 and price is not None:
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try:
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change_7d_pct = round((float(price) - float(closes[-6])) / float(closes[-6]) * 100, 3)
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except Exception:
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change_7d_pct = None
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out = {
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"ticker": key,
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"name": name,
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"price": float(price) if price is not None else None,
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"change_24h_pct": change_24h_pct,
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"change_7d_pct": change_7d_pct,
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"source": f"yfinance:{symbol}",
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"data_timestamp": datetime.now(UTC).isoformat(),
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}
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_ASSET_CACHE[key] = {"data": out, "ts": now}
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return out
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_TREASURY_CACHE: dict[str, Any] = {"data": None, "ts": 0.0}
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_TREASURY_TTL = 300.0
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async def fetch_treasury_yields() -> dict[str, Any]:
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import time
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now = time.monotonic()
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if _TREASURY_CACHE["data"] and (now - _TREASURY_CACHE["ts"]) < _TREASURY_TTL:
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return _TREASURY_CACHE["data"]
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symbols = [
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("us2y", "^UST2YR"),
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("us5y", "^FVX"),
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("us10y", "^TNX"),
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("us30y", "^TYX"),
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]
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yields: dict[str, float | None] = {}
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async with async_client(timeout=10.0) as client:
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for key, sym in symbols:
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info = await _fetch_yahoo_meta(client, sym, "5d")
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meta = info.get("meta") or {}
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price = meta.get("regularMarketPrice")
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yields[key] = float(price) if price is not None else None
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spread = None
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if yields.get("us10y") is not None and yields.get("us2y") is not None:
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spread = round(yields["us10y"] - yields["us2y"], 3)
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shape = "unknown"
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if spread is not None:
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if spread > 0.25:
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shape = "normal"
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elif spread < -0.1:
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shape = "inverted"
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else:
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shape = "flat"
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out = {
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"yields": yields,
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"spread_2y10y": spread,
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"yield_curve_shape": shape,
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"data_timestamp": datetime.now(UTC).isoformat(),
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}
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_TREASURY_CACHE["data"] = out
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_TREASURY_CACHE["ts"] = now
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return out
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def yield_curve_metrics(yields: dict[str, float | None]) -> dict[str, Any]:
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"""Slope + convexity da curva yields (us2y, us5y, us10y, us30y).
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Convexity (butterfly): 2*us10y - us2y - us30y. >0 = curva concava.
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"""
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y2 = yields.get("us2y")
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y5 = yields.get("us5y")
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y10 = yields.get("us10y")
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y30 = yields.get("us30y")
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slope_2y10y = (y10 - y2) if (y2 is not None and y10 is not None) else None
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slope_5y30y = (y30 - y5) if (y5 is not None and y30 is not None) else None
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butterfly_2_10_30 = (2 * y10 - y2 - y30) if (y2 is not None and y10 is not None and y30 is not None) else None
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regime = "unknown"
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if slope_2y10y is not None:
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if slope_2y10y >= 0.5:
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regime = "steep"
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elif slope_2y10y > 0.1:
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regime = "normal"
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elif slope_2y10y > -0.1:
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regime = "flat"
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else:
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regime = "inverted"
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return {
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"slope_2y10y": round(slope_2y10y, 3) if slope_2y10y is not None else None,
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"slope_5y30y": round(slope_5y30y, 3) if slope_5y30y is not None else None,
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"butterfly_2_10_30": round(butterfly_2_10_30, 3) if butterfly_2_10_30 is not None else None,
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"regime": regime,
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}
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|
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async def fetch_yield_curve_slope() -> dict[str, Any]:
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"""Curve slope/convexity metrics su treasury yields correnti."""
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base = await fetch_treasury_yields()
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metrics = yield_curve_metrics(base.get("yields") or {})
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return {
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"yields": base.get("yields"),
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**metrics,
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"data_timestamp": datetime.now(UTC).isoformat(),
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}
|
||||
|
||||
|
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async def fetch_breakeven_inflation(fred_api_key: str = "") -> dict[str, Any]:
|
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"""Breakeven inflation rate via FRED:
|
||||
- T10YIE (10Y breakeven, market expectation 10Y inflation)
|
||||
- T5YIE (5Y breakeven)
|
||||
- T5YIFR (5Y forward 5Y forward inflation expectation)
|
||||
"""
|
||||
if not fred_api_key:
|
||||
return {"error": "No FRED API key configured", "breakevens": {}}
|
||||
|
||||
series_map = {
|
||||
"be_5y": "T5YIE",
|
||||
"be_10y": "T10YIE",
|
||||
"be_5y5y_forward": "T5YIFR",
|
||||
}
|
||||
out: dict[str, float | None] = {}
|
||||
async with async_client(timeout=10.0) as client:
|
||||
for name, series_id in series_map.items():
|
||||
resp = await client.get(
|
||||
FRED_BASE,
|
||||
params={
|
||||
"series_id": series_id,
|
||||
"api_key": fred_api_key,
|
||||
"file_type": "json",
|
||||
"sort_order": "desc",
|
||||
"limit": 1,
|
||||
},
|
||||
)
|
||||
data = resp.json()
|
||||
obs = data.get("observations", [])
|
||||
try:
|
||||
out[name] = float(obs[0]["value"]) if obs and obs[0]["value"] != "." else None
|
||||
except (ValueError, IndexError, KeyError):
|
||||
out[name] = None
|
||||
|
||||
interpretation = "unknown"
|
||||
be10 = out.get("be_10y")
|
||||
if be10 is not None:
|
||||
if be10 > 3.0:
|
||||
interpretation = "high_inflation_expected"
|
||||
elif be10 < 1.5:
|
||||
interpretation = "low_inflation_expected"
|
||||
else:
|
||||
interpretation = "anchored"
|
||||
|
||||
return {
|
||||
"breakevens": out,
|
||||
"interpretation": interpretation,
|
||||
"data_timestamp": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
|
||||
|
||||
async def fetch_equity_futures() -> dict[str, Any]:
|
||||
"""Fetch ES/NQ/YM/RTY futures con session detection."""
|
||||
tickers = [("es", "ES=F"), ("nq", "NQ=F"), ("ym", "YM=F"), ("rty", "RTY=F")]
|
||||
now = datetime.now(UTC)
|
||||
weekday = now.weekday() # 0=Mon
|
||||
hour_utc = now.hour
|
||||
cash_open = (weekday < 5) and (13 <= hour_utc < 20)
|
||||
if cash_open:
|
||||
session = "regular"
|
||||
elif weekday >= 5:
|
||||
session = "weekend"
|
||||
elif hour_utc < 13:
|
||||
session = "pre-market"
|
||||
else:
|
||||
session = "after-hours"
|
||||
|
||||
out: dict[str, Any] = {}
|
||||
async with async_client(timeout=10.0) as client:
|
||||
for key, sym in tickers:
|
||||
info = await _fetch_yahoo_meta(client, sym, "5d")
|
||||
meta = info.get("meta") or {}
|
||||
price = meta.get("regularMarketPrice")
|
||||
prev = meta.get("previousClose") or meta.get("chartPreviousClose")
|
||||
change_pct = None
|
||||
if price is not None and prev:
|
||||
try:
|
||||
change_pct = round((float(price) - float(prev)) / float(prev) * 100, 3)
|
||||
except Exception:
|
||||
change_pct = None
|
||||
out[key] = {
|
||||
"price": float(price) if price is not None else None,
|
||||
"change_pct": change_pct,
|
||||
"session": session,
|
||||
}
|
||||
|
||||
next_open = None
|
||||
if weekday < 5 and hour_utc < 13:
|
||||
next_open = now.replace(hour=13, minute=30, second=0, microsecond=0).isoformat()
|
||||
else:
|
||||
days_ahead = (7 - weekday) if weekday >= 5 else 1
|
||||
nd = (now.replace(hour=13, minute=30, second=0, microsecond=0) + timedelta(days=days_ahead))
|
||||
next_open = nd.isoformat()
|
||||
|
||||
return {
|
||||
"futures": out,
|
||||
"session_status": {
|
||||
"cash_open": cash_open,
|
||||
"session": session,
|
||||
"next_open_utc": next_open,
|
||||
},
|
||||
"data_timestamp": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
|
||||
_MARKET_CACHE: dict[str, Any] = {"data": None, "ts": 0.0}
|
||||
_MARKET_CACHE_TTL = 120.0
|
||||
|
||||
|
||||
async def _fetch_yahoo_price(client: httpx.AsyncClient, symbol: str) -> float | None:
|
||||
try:
|
||||
resp = await client.get(
|
||||
YAHOO_CHART.format(symbol=symbol),
|
||||
params={"interval": "1d", "range": "5d"},
|
||||
headers={"User-Agent": "Mozilla/5.0"},
|
||||
)
|
||||
if resp.status_code != 200:
|
||||
return None
|
||||
result = (resp.json().get("chart") or {}).get("result") or []
|
||||
if not result:
|
||||
return None
|
||||
price = (result[0].get("meta") or {}).get("regularMarketPrice")
|
||||
return float(price) if price is not None else None
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
async def _fetch_dvol_latest(client: httpx.AsyncClient, currency: str) -> float | None:
|
||||
now_ms = int(datetime.now(UTC).timestamp() * 1000)
|
||||
start_ms = now_ms - 7 * 24 * 3600 * 1000
|
||||
try:
|
||||
resp = await client.get(
|
||||
DERIBIT_DVOL,
|
||||
params={
|
||||
"currency": currency,
|
||||
"start_timestamp": start_ms,
|
||||
"end_timestamp": now_ms,
|
||||
"resolution": "1D",
|
||||
},
|
||||
)
|
||||
rows = (resp.json().get("result") or {}).get("data") or []
|
||||
if not rows:
|
||||
return None
|
||||
return float(rows[-1][4])
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
async def fetch_economic_indicators(
|
||||
fred_api_key: str = "",
|
||||
indicators: list[str] | None = None,
|
||||
) -> dict[str, Any]:
|
||||
series_map = {
|
||||
"fed_rate": "FEDFUNDS",
|
||||
"cpi": "CPIAUCSL",
|
||||
"unemployment": "UNRATE",
|
||||
"us10y_yield": "DGS10",
|
||||
}
|
||||
result: dict[str, Any] = {}
|
||||
if not fred_api_key:
|
||||
return {"indicators": result, "error": "No FRED API key configured"}
|
||||
async with async_client(timeout=10.0) as client:
|
||||
for name, series_id in series_map.items():
|
||||
if indicators and name not in indicators:
|
||||
continue
|
||||
resp = await client.get(
|
||||
FRED_BASE,
|
||||
params={
|
||||
"series_id": series_id,
|
||||
"api_key": fred_api_key,
|
||||
"file_type": "json",
|
||||
"sort_order": "desc",
|
||||
"limit": 1,
|
||||
},
|
||||
)
|
||||
data = resp.json()
|
||||
obs = data.get("observations", [])
|
||||
result[name] = float(obs[0]["value"]) if obs else None
|
||||
result["updated_at"] = datetime.now(UTC).isoformat()
|
||||
return result
|
||||
|
||||
|
||||
CURRENCY_TO_COUNTRY = {
|
||||
"USD": ("US", "United States"),
|
||||
"EUR": ("EU", "Euro Area"),
|
||||
"JPY": ("JP", "Japan"),
|
||||
"GBP": ("UK", "United Kingdom"),
|
||||
"CAD": ("CA", "Canada"),
|
||||
"AUD": ("AU", "Australia"),
|
||||
"NZD": ("NZ", "New Zealand"),
|
||||
"CHF": ("CH", "Switzerland"),
|
||||
"CNY": ("CN", "China"),
|
||||
}
|
||||
|
||||
_HIGH_IMPACT_EVENTS = (
|
||||
"fomc", "fed", "cpi", "nfp", "non-farm", "nonfarm", "ppi",
|
||||
"ecb", "boj", "boe", "gdp", "unemployment rate",
|
||||
)
|
||||
|
||||
|
||||
def _market_impact_historical(name: str) -> str:
|
||||
n = (name or "").lower()
|
||||
for kw in _HIGH_IMPACT_EVENTS:
|
||||
if kw in n:
|
||||
return "high_vol_spike"
|
||||
return "normal"
|
||||
|
||||
|
||||
async def fetch_macro_calendar(
|
||||
finnhub_api_key: str = "",
|
||||
days_ahead: int = 7,
|
||||
country_filter: list[str] | None = None,
|
||||
importance_min: str | None = None,
|
||||
start: str | None = None,
|
||||
end: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""Fetch economic calendar con filtri country/importance/date range."""
|
||||
events: list[dict[str, Any]] = []
|
||||
|
||||
importance_order = {"low": 0, "medium": 1, "high": 2}
|
||||
min_level = importance_order.get(
|
||||
(importance_min or "").lower(), 0
|
||||
) if importance_min else 0
|
||||
|
||||
start_dt: datetime | None = None
|
||||
end_dt: datetime | None = None
|
||||
if start:
|
||||
try:
|
||||
start_dt = datetime.fromisoformat(start).replace(tzinfo=UTC)
|
||||
except ValueError:
|
||||
start_dt = datetime.strptime(start, "%Y-%m-%d").replace(tzinfo=UTC)
|
||||
if end:
|
||||
try:
|
||||
end_dt = datetime.fromisoformat(end).replace(tzinfo=UTC)
|
||||
except ValueError:
|
||||
end_dt = datetime.strptime(end, "%Y-%m-%d").replace(tzinfo=UTC)
|
||||
|
||||
country_filter_set = (
|
||||
{c.upper() for c in country_filter} if country_filter else None
|
||||
)
|
||||
|
||||
# Try Forex Factory free feed first
|
||||
try:
|
||||
async with async_client(timeout=10.0) as client:
|
||||
resp = await client.get("https://nfs.faireconomy.media/ff_calendar_thisweek.json")
|
||||
if resp.status_code == 200:
|
||||
raw = resp.json()
|
||||
now = datetime.now(UTC)
|
||||
for e in raw:
|
||||
date_str = e.get("date", "")
|
||||
event_dt: datetime | None = None
|
||||
try:
|
||||
event_dt = datetime.fromisoformat(date_str.replace("Z", "+00:00"))
|
||||
if event_dt < now:
|
||||
continue
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
currency = (e.get("country", "") or "").upper()
|
||||
country_code, country_name = CURRENCY_TO_COUNTRY.get(
|
||||
currency, (currency or "", e.get("country", "") or "")
|
||||
)
|
||||
|
||||
if country_filter_set and country_code not in country_filter_set:
|
||||
continue
|
||||
|
||||
impact = (e.get("impact", "") or "").lower()
|
||||
importance = (
|
||||
"high" if impact == "high" else "medium" if impact == "medium" else "low"
|
||||
)
|
||||
if importance_order[importance] < min_level:
|
||||
continue
|
||||
|
||||
if start_dt and event_dt and event_dt < start_dt:
|
||||
continue
|
||||
if end_dt and event_dt and event_dt > end_dt:
|
||||
continue
|
||||
|
||||
name = e.get("title", "")
|
||||
events.append(
|
||||
{
|
||||
"date": date_str,
|
||||
"datetime_utc": event_dt.isoformat() if event_dt else date_str,
|
||||
"name": name,
|
||||
"event": name,
|
||||
"country": country_name,
|
||||
"country_code": country_code,
|
||||
"importance": importance,
|
||||
"forecast": e.get("forecast", ""),
|
||||
"previous": e.get("previous", ""),
|
||||
"actual": e.get("actual"),
|
||||
"market_impact_historical": _market_impact_historical(name),
|
||||
}
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Fallback to Finnhub if we have a key and no events
|
||||
if not events and finnhub_api_key:
|
||||
try:
|
||||
now = datetime.now(UTC)
|
||||
end_default = now + timedelta(days=days_ahead)
|
||||
async with async_client(timeout=10.0) as client:
|
||||
resp = await client.get(
|
||||
FINNHUB_CALENDAR,
|
||||
params={
|
||||
"from": (start_dt or now).strftime("%Y-%m-%d"),
|
||||
"to": (end_dt or end_default).strftime("%Y-%m-%d"),
|
||||
"token": finnhub_api_key,
|
||||
},
|
||||
)
|
||||
data = resp.json()
|
||||
if isinstance(data, dict) and "error" in data:
|
||||
return {"events": [], "error": data["error"]}
|
||||
raw = data if isinstance(data, list) else data.get("economicCalendar", [])
|
||||
for e in raw:
|
||||
importance_raw = (
|
||||
e.get("importance")
|
||||
or e.get("impact")
|
||||
or "medium"
|
||||
)
|
||||
if isinstance(importance_raw, int):
|
||||
importance = (
|
||||
"high" if importance_raw >= 3 else
|
||||
"medium" if importance_raw >= 2 else
|
||||
"low"
|
||||
)
|
||||
else:
|
||||
importance = str(importance_raw).lower()
|
||||
if importance not in ("low", "medium", "high"):
|
||||
importance = "medium"
|
||||
if importance_order[importance] < min_level:
|
||||
continue
|
||||
country_code = (e.get("country", "") or "").upper()
|
||||
country_name = CURRENCY_TO_COUNTRY.get(
|
||||
country_code, (country_code, country_code)
|
||||
)[1]
|
||||
if country_filter_set and country_code not in country_filter_set:
|
||||
continue
|
||||
name = e.get("event", "")
|
||||
date_str = e.get("date", e.get("time", ""))
|
||||
events.append({
|
||||
"date": date_str,
|
||||
"datetime_utc": date_str,
|
||||
"name": name,
|
||||
"event": name,
|
||||
"country": country_name,
|
||||
"country_code": country_code,
|
||||
"importance": importance,
|
||||
"forecast": e.get("forecast", ""),
|
||||
"previous": e.get("previous", e.get("prev", "")),
|
||||
"actual": e.get("actual"),
|
||||
"market_impact_historical": _market_impact_historical(name),
|
||||
})
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if not events:
|
||||
return {"events": [], "note": "No calendar source available"}
|
||||
|
||||
return {"events": events}
|
||||
|
||||
|
||||
async def fetch_market_overview() -> dict[str, Any]:
|
||||
import time
|
||||
|
||||
now = time.monotonic()
|
||||
if _MARKET_CACHE["data"] is not None and (now - _MARKET_CACHE["ts"]) < _MARKET_CACHE_TTL:
|
||||
return _MARKET_CACHE["data"]
|
||||
|
||||
async with async_client(timeout=10.0) as client:
|
||||
global_data: dict[str, Any] = {}
|
||||
prices: dict[str, Any] = {}
|
||||
try:
|
||||
global_resp = await client.get(COINGECKO_GLOBAL)
|
||||
global_data = global_resp.json().get("data", {}) or {}
|
||||
except Exception:
|
||||
global_data = {}
|
||||
try:
|
||||
price_resp = await client.get(
|
||||
COINGECKO_SIMPLE,
|
||||
params={"ids": "bitcoin,ethereum", "vs_currencies": "usd"},
|
||||
)
|
||||
prices = price_resp.json() or {}
|
||||
except Exception:
|
||||
prices = {}
|
||||
dvol_btc = await _fetch_dvol_latest(client, "BTC")
|
||||
dvol_eth = await _fetch_dvol_latest(client, "ETH")
|
||||
sp500 = await _fetch_yahoo_price(client, "^GSPC")
|
||||
gold = await _fetch_yahoo_price(client, "GC=F")
|
||||
vix = await _fetch_yahoo_price(client, "^VIX")
|
||||
|
||||
out = {
|
||||
"btc_dominance": global_data.get("market_cap_percentage", {}).get("btc"),
|
||||
"total_market_cap": global_data.get("total_market_cap", {}).get("usd"),
|
||||
"btc_price": prices.get("bitcoin", {}).get("usd"),
|
||||
"eth_price": prices.get("ethereum", {}).get("usd"),
|
||||
"sp500": sp500,
|
||||
"gold": gold,
|
||||
"vix": vix,
|
||||
"dvol_btc": dvol_btc,
|
||||
"dvol_eth": dvol_eth,
|
||||
"data_timestamp": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
_MARKET_CACHE["data"] = out
|
||||
_MARKET_CACHE["ts"] = now
|
||||
return out
|
||||
|
||||
|
||||
_COT_TTL = 3600.0 # 1h
|
||||
_COT_CACHE: dict[tuple[str, str, int], dict[str, Any]] = {}
|
||||
_COT_CACHE_TS: dict[tuple[str, str, int], float] = {}
|
||||
|
||||
|
||||
async def fetch_cot_tff(symbol: str, lookback_weeks: int = 52) -> dict[str, Any]:
|
||||
"""Fetch COT TFF report per simbolo equity/financial. Returns ASC by date."""
|
||||
import time
|
||||
|
||||
symbol = symbol.upper()
|
||||
if symbol not in SYMBOL_TO_CFTC_CODE_TFF:
|
||||
return {"error": "unknown_symbol", "available": ALL_TFF_SYMBOLS}
|
||||
|
||||
key = (symbol, "tff", lookback_weeks)
|
||||
now = time.monotonic()
|
||||
if key in _COT_CACHE and (now - _COT_CACHE_TS[key]) < _COT_TTL:
|
||||
return _COT_CACHE[key]
|
||||
|
||||
code = SYMBOL_TO_CFTC_CODE_TFF[symbol]
|
||||
url = f"{CFTC_BASE_URL}/{TFF_DATASET_ID}.json"
|
||||
async with async_client(timeout=10.0) as client:
|
||||
resp = await client.get(
|
||||
url,
|
||||
params={
|
||||
"cftc_contract_market_code": code,
|
||||
"$order": "report_date_as_yyyy_mm_dd DESC",
|
||||
"$limit": str(lookback_weeks),
|
||||
},
|
||||
)
|
||||
if resp.status_code != 200:
|
||||
return {"symbol": symbol, "report_type": "tff", "rows": [], "error": "cftc_unavailable"}
|
||||
raw_rows = resp.json() or []
|
||||
parsed = [parse_tff_row(r) for r in raw_rows]
|
||||
parsed.sort(key=lambda r: r["report_date"]) # ASC by date
|
||||
out = {
|
||||
"symbol": symbol,
|
||||
"report_type": "tff",
|
||||
"rows": parsed,
|
||||
"data_timestamp": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
_COT_CACHE[key] = out
|
||||
_COT_CACHE_TS[key] = now
|
||||
return out
|
||||
|
||||
|
||||
async def fetch_cot_disaggregated(symbol: str, lookback_weeks: int = 52) -> dict[str, Any]:
|
||||
"""Fetch COT Disaggregated report per simbolo commodity. Returns ASC by date."""
|
||||
import time
|
||||
|
||||
symbol = symbol.upper()
|
||||
if symbol not in SYMBOL_TO_CFTC_CODE_DISAGG:
|
||||
return {"error": "unknown_symbol", "available": ALL_DISAGG_SYMBOLS}
|
||||
|
||||
key = (symbol, "disaggregated", lookback_weeks)
|
||||
now = time.monotonic()
|
||||
if key in _COT_CACHE and (now - _COT_CACHE_TS[key]) < _COT_TTL:
|
||||
return _COT_CACHE[key]
|
||||
|
||||
code = SYMBOL_TO_CFTC_CODE_DISAGG[symbol]
|
||||
url = f"{CFTC_BASE_URL}/{DISAGG_DATASET_ID}.json"
|
||||
async with async_client(timeout=10.0) as client:
|
||||
resp = await client.get(
|
||||
url,
|
||||
params={
|
||||
"cftc_contract_market_code": code,
|
||||
"$order": "report_date_as_yyyy_mm_dd DESC",
|
||||
"$limit": str(lookback_weeks),
|
||||
},
|
||||
)
|
||||
if resp.status_code != 200:
|
||||
return {"symbol": symbol, "report_type": "disaggregated", "rows": [], "error": "cftc_unavailable"}
|
||||
raw_rows = resp.json() or []
|
||||
parsed = [parse_disagg_row(r) for r in raw_rows]
|
||||
parsed.sort(key=lambda r: r["report_date"])
|
||||
out = {
|
||||
"symbol": symbol,
|
||||
"report_type": "disaggregated",
|
||||
"rows": parsed,
|
||||
"data_timestamp": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
_COT_CACHE[key] = out
|
||||
_COT_CACHE_TS[key] = now
|
||||
return out
|
||||
|
||||
|
||||
async def fetch_cot_extreme_positioning(lookback_weeks: int = 156) -> dict[str, Any]:
|
||||
"""Scanner posizionamento estremo (percentile <=5 o >=95) sui simboli watchlist.
|
||||
|
||||
TFF -> key_role = lev_funds (lev_funds_net).
|
||||
Disaggregated -> key_role = managed_money (managed_money_net).
|
||||
"""
|
||||
import asyncio
|
||||
|
||||
tff_tasks = [fetch_cot_tff(s, lookback_weeks) for s in ALL_TFF_SYMBOLS]
|
||||
disagg_tasks = [fetch_cot_disaggregated(s, lookback_weeks) for s in ALL_DISAGG_SYMBOLS]
|
||||
tff_results, disagg_results = await asyncio.gather(
|
||||
asyncio.gather(*tff_tasks, return_exceptions=True),
|
||||
asyncio.gather(*disagg_tasks, return_exceptions=True),
|
||||
)
|
||||
|
||||
extremes: list[dict[str, Any]] = []
|
||||
|
||||
for res in tff_results:
|
||||
if isinstance(res, BaseException) or not isinstance(res, dict):
|
||||
continue
|
||||
rows = res.get("rows") or []
|
||||
if len(rows) < 4:
|
||||
continue
|
||||
series = [r["lev_funds_net"] for r in rows]
|
||||
current = series[-1]
|
||||
history = series[:-1]
|
||||
pct = compute_percentile(current, history)
|
||||
extremes.append({
|
||||
"symbol": res["symbol"],
|
||||
"report_type": "tff",
|
||||
"key_role": "lev_funds",
|
||||
"current_net": current,
|
||||
"percentile": pct,
|
||||
"signal": classify_extreme(pct),
|
||||
"report_date": rows[-1]["report_date"],
|
||||
})
|
||||
|
||||
for res in disagg_results:
|
||||
if isinstance(res, BaseException) or not isinstance(res, dict):
|
||||
continue
|
||||
rows = res.get("rows") or []
|
||||
if len(rows) < 4:
|
||||
continue
|
||||
series = [r["managed_money_net"] for r in rows]
|
||||
current = series[-1]
|
||||
history = series[:-1]
|
||||
pct = compute_percentile(current, history)
|
||||
extremes.append({
|
||||
"symbol": res["symbol"],
|
||||
"report_type": "disaggregated",
|
||||
"key_role": "managed_money",
|
||||
"current_net": current,
|
||||
"percentile": pct,
|
||||
"signal": classify_extreme(pct),
|
||||
"report_date": rows[-1]["report_date"],
|
||||
})
|
||||
|
||||
return {
|
||||
"lookback_weeks": lookback_weeks,
|
||||
"extremes": extremes,
|
||||
"data_timestamp": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
@@ -0,0 +1,154 @@
|
||||
"""Tool macro V2: pydantic schemas + async functions.
|
||||
|
||||
Ogni funzione prende (client: MacroClient, params: <Req>) e ritorna un dict.
|
||||
Pure logica, no FastAPI dependency, no ACL. Macro è read-only data provider:
|
||||
nessun write tool, nessun leverage_cap.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from cerbero_mcp.exchanges.macro.client import MacroClient
|
||||
from cerbero_mcp.exchanges.macro.fetchers import (
|
||||
fetch_asset_price,
|
||||
fetch_breakeven_inflation,
|
||||
fetch_cot_disaggregated,
|
||||
fetch_cot_extreme_positioning,
|
||||
fetch_cot_tff,
|
||||
fetch_economic_indicators,
|
||||
fetch_equity_futures,
|
||||
fetch_macro_calendar,
|
||||
fetch_market_overview,
|
||||
fetch_treasury_yields,
|
||||
fetch_yield_curve_slope,
|
||||
)
|
||||
|
||||
# === Schemas ===
|
||||
|
||||
|
||||
class GetEconomicIndicatorsReq(BaseModel):
|
||||
indicators: list[str] | None = None
|
||||
|
||||
|
||||
class GetMacroCalendarReq(BaseModel):
|
||||
days: int = 7
|
||||
country_filter: list[str] | None = None
|
||||
importance_min: str | None = None
|
||||
start: str | None = None
|
||||
end: str | None = None
|
||||
|
||||
|
||||
class GetMarketOverviewReq(BaseModel):
|
||||
pass
|
||||
|
||||
|
||||
class GetAssetPriceReq(BaseModel):
|
||||
ticker: str
|
||||
|
||||
|
||||
class GetTreasuryYieldsReq(BaseModel):
|
||||
pass
|
||||
|
||||
|
||||
class GetEquityFuturesReq(BaseModel):
|
||||
pass
|
||||
|
||||
|
||||
class GetYieldCurveSlopeReq(BaseModel):
|
||||
pass
|
||||
|
||||
|
||||
class GetBreakevenInflationReq(BaseModel):
|
||||
pass
|
||||
|
||||
|
||||
class GetCotTffReq(BaseModel):
|
||||
symbol: str
|
||||
lookback_weeks: int = Field(default=52, ge=4, le=520)
|
||||
|
||||
|
||||
class GetCotDisaggregatedReq(BaseModel):
|
||||
symbol: str
|
||||
lookback_weeks: int = Field(default=52, ge=4, le=520)
|
||||
|
||||
|
||||
class GetCotExtremeReq(BaseModel):
|
||||
lookback_weeks: int = Field(default=156, ge=4, le=520)
|
||||
|
||||
|
||||
# === Tool functions ===
|
||||
|
||||
|
||||
async def get_economic_indicators(
|
||||
client: MacroClient, params: GetEconomicIndicatorsReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_economic_indicators(
|
||||
fred_api_key=client.fred_api_key, indicators=params.indicators
|
||||
)
|
||||
|
||||
|
||||
async def get_macro_calendar(
|
||||
client: MacroClient, params: GetMacroCalendarReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_macro_calendar(
|
||||
finnhub_api_key=client.finnhub_api_key,
|
||||
days_ahead=params.days,
|
||||
country_filter=params.country_filter,
|
||||
importance_min=params.importance_min,
|
||||
start=params.start,
|
||||
end=params.end,
|
||||
)
|
||||
|
||||
|
||||
async def get_market_overview(
|
||||
client: MacroClient, params: GetMarketOverviewReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_market_overview()
|
||||
|
||||
|
||||
async def get_asset_price(
|
||||
client: MacroClient, params: GetAssetPriceReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_asset_price(params.ticker)
|
||||
|
||||
|
||||
async def get_treasury_yields(
|
||||
client: MacroClient, params: GetTreasuryYieldsReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_treasury_yields()
|
||||
|
||||
|
||||
async def get_equity_futures(
|
||||
client: MacroClient, params: GetEquityFuturesReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_equity_futures()
|
||||
|
||||
|
||||
async def get_yield_curve_slope(
|
||||
client: MacroClient, params: GetYieldCurveSlopeReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_yield_curve_slope()
|
||||
|
||||
|
||||
async def get_breakeven_inflation(
|
||||
client: MacroClient, params: GetBreakevenInflationReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_breakeven_inflation(fred_api_key=client.fred_api_key)
|
||||
|
||||
|
||||
async def get_cot_tff(client: MacroClient, params: GetCotTffReq) -> dict[str, Any]:
|
||||
return await fetch_cot_tff(params.symbol, params.lookback_weeks)
|
||||
|
||||
|
||||
async def get_cot_disaggregated(
|
||||
client: MacroClient, params: GetCotDisaggregatedReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_cot_disaggregated(params.symbol, params.lookback_weeks)
|
||||
|
||||
|
||||
async def get_cot_extreme_positioning(
|
||||
client: MacroClient, params: GetCotExtremeReq
|
||||
) -> dict[str, Any]:
|
||||
return await fetch_cot_extreme_positioning(params.lookback_weeks)
|
||||
Reference in New Issue
Block a user