merge origin/main (discovery strumenti downloader) in shape+portfolios
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+12
-1
@@ -69,8 +69,19 @@ def _fetch_binance(symbol: str, tf: str, since_ms: int, limit: int = 1000) -> li
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def _download_cerbero_range(
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instrument: str, resolution: str, tf: str, start_date: str, end_date: str
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instrument: str, resolution: str, tf: str, start_date: str, end_date: str,
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allow_unvalidated: bool = False,
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) -> pd.DataFrame:
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# Gate: si raccolgono dati SOLO per strumenti validati nel registry.
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# Esegui `python -m src.data.instruments` per (ri)costruirlo.
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if not allow_unvalidated:
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from src.data.instruments import is_validated
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if not is_validated(instrument, tf, "deribit"):
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raise ValueError(
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f"Strumento non validato: {instrument} @ {tf}. "
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f"Costruisci il registry (python -m src.data.instruments) o passa "
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f"allow_unvalidated=True per forzare."
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)
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all_candles: list[dict] = []
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max_days = MAX_DAYS_PER_REQUEST[tf]
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current = datetime.fromisoformat(start_date)
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@@ -0,0 +1,278 @@
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"""Discovery + validazione strumenti per gli exchange implementati (via Cerbero MCP).
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Per ogni exchange (Deribit, Hyperliquid — esclusi Alpaca/stocks e Bybit, il cui
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feed testnet e' farlocco) enumera i perpetui, ne verifica i dati e produce un
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registry di strumenti VALIDATI.
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Solo gli strumenti nel registry possono essere usati per la raccolta dati
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(vedi gate in src/data/downloader.py).
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Controlli di validazione (uno strumento e' valido solo se TUTTI passano):
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- exists : la storia daily ritorna candele
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- ohlc_sane : high>=low, high>=max(o,c), low<=min(o,c), prezzi>0
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- not_flat : non e' un contratto morto (quasi tutte le barre O==H==L==C)
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- liquid : volume_24h>0 dal ticker
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- congruent : il prezzo concorda (entro tolleranza) con la MEDIANA dello
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stesso base-coin su tutti gli exchange. Scarta i feed testnet
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farlocchi (es. Bybit BTC=300k) e i contratti sbagliati
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(es. Deribit SOL-PERPETUAL=9.6 vs SOL reale ~82).
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NB: il token Cerbero punta a TESTNET; la congruenza cross-exchange e' il filtro
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che distingue i feed realistici (Deribit, Hyperliquid) da quelli farlocchi.
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"""
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from __future__ import annotations
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import json
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import statistics
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from pathlib import Path
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import pandas as pd
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from src.live.cerbero_client import CerberoClient
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REGISTRY_PATH = Path(__file__).resolve().parents[2] / "data" / "instruments_registry.json"
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# I nostri timestep -> codice risoluzione per ciascun exchange
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TF_CODES = {
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"deribit": {"1m": "1", "5m": "5", "15m": "15", "1h": "60", "1d": "1D"},
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"hyperliquid": {"1m": "1m", "5m": "5m", "15m": "15m", "1h": "1h", "1d": "1d"},
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}
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CONGRUENCE_TOL = 0.05 # 5% di scostamento dalla mediana del base-coin
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def _today() -> str:
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return datetime.now(timezone.utc).strftime("%Y-%m-%d")
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@dataclass
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class Quote:
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base: str
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symbol: str
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last: float | None = None
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volume_24h: float | None = None
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open_interest: float | None = None
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# --------------------------- adapters ---------------------------
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class ExchangeAdapter:
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name = "base"
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def __init__(self, client: CerberoClient):
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self.c = client
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def _post(self, tool: str, payload: dict) -> dict:
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return self.c._post(f"/mcp-{self.name}/tools/{tool}", payload)
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def list_symbols(self) -> list[Quote]:
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"""Lista perpetui (economica). I prezzi possono essere None (vedi ticker)."""
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raise NotImplementedError
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def ticker(self, q: Quote) -> None:
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"""Riempie last/volume/OI sul Quote (per-simbolo). No-op se gia' pieni."""
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def candles(self, symbol: str, tf: str, start: str, end: str) -> pd.DataFrame:
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raise NotImplementedError
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class DeribitAdapter(ExchangeAdapter):
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name = "deribit"
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def list_symbols(self) -> list[Quote]:
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perps, offset = [], 0
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while True:
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r = self._post("get_instruments", {"currency": "any", "kind": "future",
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"offset": offset, "limit": 100})
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insts = r.get("instruments", [])
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perps += [i["name"] for i in insts if i.get("name", "").endswith("-PERPETUAL")]
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if not r.get("has_more") or not insts:
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break
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offset += len(insts)
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if offset > 2000:
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break
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out = []
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for name in perps:
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base = name.split("-PERPETUAL")[0].replace("_USDC", "").replace("_USD", "")
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out.append(Quote(base, name))
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return out
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def ticker(self, q: Quote) -> None:
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t = self._post("get_ticker", {"instrument": q.symbol})
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q.last, q.volume_24h, q.open_interest = t.get("last_price"), t.get("volume_24h"), t.get("open_interest")
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def candles(self, symbol, tf, start, end) -> pd.DataFrame:
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r = self._post("get_historical", {"instrument": symbol, "start_date": start,
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"end_date": end, "resolution": TF_CODES["deribit"][tf]})
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return _to_df(r.get("candles", []))
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class HyperliquidAdapter(ExchangeAdapter):
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name = "hyperliquid"
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def list_symbols(self) -> list[Quote]:
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r = self._post("get_markets", {})
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markets = r if isinstance(r, list) else r.get("markets", [])
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return [Quote(m["asset"], m["asset"], m.get("mark_price"),
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m.get("volume_24h"), m.get("open_interest")) for m in markets]
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# prezzi gia' presenti da get_markets -> ticker no-op
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def candles(self, symbol, tf, start, end) -> pd.DataFrame:
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r = self._post("get_historical", {"asset": symbol, "start_date": start, "end_date": end,
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"resolution": TF_CODES["hyperliquid"][tf], "limit": 5000})
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return _to_df(r.get("candles", []))
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ADAPTERS = {"deribit": DeribitAdapter, "hyperliquid": HyperliquidAdapter}
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def _to_df(candles: list[dict]) -> pd.DataFrame:
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if not candles:
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return pd.DataFrame()
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df = pd.DataFrame(candles)
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df["timestamp"] = df["timestamp"].astype("int64")
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return df.sort_values("timestamp").reset_index(drop=True)
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# --------------------------- validazione ---------------------------
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def _ohlc_sane(df: pd.DataFrame) -> bool:
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if df.empty:
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return False
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o, h, l, c = df["open"], df["high"], df["low"], df["close"]
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ok = (h >= l) & (h >= o) & (h >= c) & (l <= o) & (l <= c) & (c > 0) & (l > 0)
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return bool(ok.mean() > 0.99)
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def _not_flat(df: pd.DataFrame) -> bool:
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if df.empty:
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return False
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flat = (df["open"] == df["high"]) & (df["high"] == df["low"]) & (df["low"] == df["close"])
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return bool(flat.mean() < 0.90)
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def build_registry(exchanges: list[str] | None = None,
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tf_check: tuple[str, ...] = ("1m", "5m", "15m", "1h"),
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start_scan_from: str = "2017-01-01",
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save: bool = True) -> dict:
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exchanges = exchanges or ["deribit", "hyperliquid"] # NO alpaca, NO bybit (testnet farlocco)
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client = CerberoClient()
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adapters = {ex: ADAPTERS[ex](client) for ex in exchanges}
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# 1) lista economica per ogni exchange
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listed: dict[str, list[Quote]] = {}
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for ex, ad in adapters.items():
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try:
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listed[ex] = ad.list_symbols()
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print(f" [{ex}] {len(listed[ex])} strumenti elencati")
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except Exception as e:
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print(f" [{ex}] discovery FALLITA: {type(e).__name__}: {e}")
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listed[ex] = []
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# 2) universo = base-coin presenti su Deribit (il nostro venue). Bybit/HL
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# vengono validati solo sull'overlap (cross-check), non su 500+ simboli.
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deribit_bases = {q.base for q in listed.get("deribit", [])}
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selected: dict[str, list[Quote]] = {}
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for ex, qs in listed.items():
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selected[ex] = qs if ex == "deribit" else [q for q in qs if q.base in deribit_bases]
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# 3) timeframe disponibili per exchange (testati su BTC recente)
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ref = {"deribit": "BTC-PERPETUAL", "hyperliquid": "BTC"}
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tf_by_ex: dict[str, list[str]] = {}
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for ex, ad in adapters.items():
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oks = []
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for tf in tf_check:
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try:
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if not ad.candles(ref[ex], tf, _today(), _today()).empty:
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oks.append(tf)
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except Exception:
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pass
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tf_by_ex[ex] = oks
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print(f" [{ex}] timeframe ok: {oks}")
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# 4) UNA fetch daily per strumento: e' il dato che davvero raccoglieremmo.
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# Da qui ricaviamo esistenza, OHLC, not-flat, start-date, prezzo-per-congruenza
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# (ultima close STORICA, non il ticker) e liquidita' (volume daily recente).
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scan: dict[tuple[str, str], dict] = {}
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for ex, ad in adapters.items():
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for q in selected[ex]:
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rec = {"reasons": [], "last_close": None, "start_date": None, "vol": 0.0}
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try:
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d = ad.candles(q.symbol, "1d", start_scan_from, _today())
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if d.empty:
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rec["reasons"].append("no_history")
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else:
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if not _ohlc_sane(d):
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rec["reasons"].append("ohlc_insane")
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if not _not_flat(d):
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rec["reasons"].append("flat_dead")
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rec["last_close"] = float(d["close"].iloc[-1])
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rec["vol"] = float(d["volume"].tail(7).mean())
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rec["start_date"] = str(pd.to_datetime(d["timestamp"].iloc[0], unit="ms", utc=True).date())
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except Exception as e:
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rec["reasons"].append(f"history_err:{type(e).__name__}")
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scan[(ex, q.symbol)] = rec
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# 5) mediana per base-coin dall'ULTIMA CLOSE STORICA (riferimento congruenza)
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by_base: dict[str, list[float]] = {}
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for (ex, sym), rec in scan.items():
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base = next(q.base for q in selected[ex] if q.symbol == sym)
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if rec["last_close"] and rec["last_close"] > 0:
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by_base.setdefault(base, []).append(rec["last_close"])
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median_px = {b: statistics.median(v) for b, v in by_base.items()}
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# 6) finalizza validazione
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registry: dict = {"generated_at": datetime.now(timezone.utc).isoformat(),
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"congruence_tol": CONGRUENCE_TOL, "testnet": True, "exchanges": {}}
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for ex, ad in adapters.items():
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registry["exchanges"][ex] = {"timeframes": tf_by_ex[ex], "instruments": {}}
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for q in selected[ex]:
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rec = scan[(ex, q.symbol)]
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reasons = list(rec["reasons"])
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px, med, n_src = rec["last_close"], median_px.get(q.base), len(by_base.get(q.base, []))
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if not (rec["vol"] and rec["vol"] > 0):
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reasons.append("no_volume")
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if px is None or px <= 0:
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if "no_history" not in reasons:
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reasons.append("no_price")
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elif med and n_src >= 2 and abs(px - med) / med > CONGRUENCE_TOL:
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reasons.append(f"incongruent(px={px:.4g},med={med:.4g})")
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valid = len(reasons) == 0
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registry["exchanges"][ex]["instruments"][q.symbol] = {
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"base": q.base, "valid": valid, "reasons": reasons,
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"last_price": px, "start_date": rec["start_date"],
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"timeframes": tf_by_ex[ex] if valid else [],
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}
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if save:
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REGISTRY_PATH.write_text(json.dumps(registry, indent=2))
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print(f" registry salvato in {REGISTRY_PATH}")
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return registry
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# --------------------------- gate per il downloader ---------------------------
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def load_registry() -> dict:
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return json.loads(REGISTRY_PATH.read_text()) if REGISTRY_PATH.exists() else {}
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def is_validated(symbol: str, tf: str, exchange: str = "deribit") -> bool:
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"""True solo se lo strumento e' nel registry come valido per quel timeframe."""
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inst = load_registry().get("exchanges", {}).get(exchange, {}).get("instruments", {}).get(symbol)
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return bool(inst and inst.get("valid") and tf in inst.get("timeframes", []))
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if __name__ == "__main__":
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reg = build_registry()
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print("\n" + "=" * 96)
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print(" REGISTRY STRUMENTI VALIDATI")
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print("=" * 96)
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for ex, exd in reg["exchanges"].items():
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insts = exd["instruments"]
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valid = {s: i for s, i in insts.items() if i["valid"]}
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print(f"\n {ex.upper()} | tf={exd['timeframes']} | validi {len(valid)}/{len(insts)}")
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for s, i in sorted(valid.items(), key=lambda kv: kv[1]["base"]):
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print(f" {s:30s} {i['base']:10s} px={i['last_price']:<12.6g} dal {i['start_date']}")
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bad = {s: i for s, i in insts.items() if not i["valid"]}
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if bad:
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shown = list(bad.items())[:6]
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print(f" -- scartati {len(bad)} (primi {len(shown)}):")
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for s, i in shown:
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print(f" {s:30s} {','.join(i['reasons'])[:64]}")
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