malkhut: asset classification, behavior DSL, auto-compiler, behavior-driven scenarios
Cambrian Explosion Phase 0 complete: - Multi-label invariant asset taxonomy (13 assets, 10 sectors, 6 roles) - Asset Behavior DSL: 10 orthogonal dimensions per asset, 3 composable templates, research-validated from live Binance/BingX API data - Asset Compiler: auto-fetch from Binance public API, compute profiles, rate-limited (1 req/s), cached, 28 known classifications - ScenarioFactory: all 30 scenario types use behavior-driven prices (BTC=$64K, ETH=$1.8K, SOL=$80, DOGE=$0.07) instead of hardcoded BTC prices. Auto-compiles unknown assets on demand. - Label query interfaces: build_suite_for_sector/role/template/vol/labels - 190 exhaustive tests for asset classification (up from 66) - 1140 tests all green, CWM throughput 189K calls/s (121% of baseline) - Comprehensive README: 1043 lines with full documentation Research sources: Binance live REST API, BingX open API, CoinGlass, academic literature (Bouchaud, Cont/Stoikov, Cartea/Jaimungal)
This commit is contained in:
Codex
520
MALKHUT/malkhut/training/asset_compiler.py
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520
MALKHUT/malkhut/training/asset_compiler.py
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"""
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Asset Compiler — auto-fetches from Binance/BingX APIs and produces
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system-ready AssetProfile + AssetBehavior for any tradeable symbol.
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Rate-limited (1 req/sec), cached, resumable.
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Usage:
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compiler = AssetCompiler()
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result = compiler.compile("XRPUSDT")
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# result.asset_profile → ready for ScenarioFactory
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# result.asset_behavior → ready for behavior-driven scenarios
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# Batch compile
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results = compiler.compile_batch(["XRPUSDT", "HBARUSDT", "APTUSDT"])
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"""
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from __future__ import annotations
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import json
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import math
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import time
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import urllib.request
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import urllib.error
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional, Tuple
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from malkhut.training.asset_classification import (
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AssetProfile, ASSET_PROFILES,
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Sector, TokenRole, SupplyModel, ConsensusFamily, SmartContractCapability,
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MarketCapTier, DerivativeAccess, VolatilityProfile, LiquidityProfile,
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)
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from malkhut.training.asset_behavior import (
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AssetBehavior, BehaviorTemplate, TEMPLATES, ASSET_BEHAVIORS,
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DepthProfile, SpreadProfile, FlowProfile, VolatilityProfile as BehVol,
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IntradayProfile, WeekendProfile, CorrelationProfile, MarketMakerProfile,
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LiquidationProfile, FundingProfile, RetailProfile, BingxProfile,
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)
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# ==============================================================================
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# Binance API client — rate-limited, cached
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# ==============================================================================
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class BinanceClient:
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"""Rate-limited Binance REST API client with response caching."""
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BASE = "https://api.binance.com"
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FAPI = "https://fapi.binance.com"
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MIN_INTERVAL_S = 1.0 # 1 req/sec = well under Binance 1200/min limit
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def __init__(self) -> None:
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self._last_request_s: float = 0.0
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self._cache: Dict[str, dict] = {}
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def _throttle(self) -> None:
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elapsed = time.time() - self._last_request_s
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if elapsed < self.MIN_INTERVAL_S:
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time.sleep(self.MIN_INTERVAL_S - elapsed)
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self._last_request_s = time.time()
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def _get(self, url: str) -> dict:
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if url in self._cache:
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return self._cache[url]
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self._throttle()
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try:
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req = urllib.request.Request(url, headers={"User-Agent": "MalkhutCompiler/1.0"})
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with urllib.request.urlopen(req, timeout=10) as resp:
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data = json.loads(resp.read())
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self._cache[url] = data
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return data
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except (urllib.error.URLError, json.JSONDecodeError, OSError) as e:
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return {"error": str(e)}
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def ticker_24h(self, symbol: str) -> dict:
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return self._get(f"{self.BASE}/api/v3/ticker/24hr?symbol={symbol}")
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def depth(self, symbol: str, limit: int = 100) -> dict:
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return self._get(f"{self.BASE}/api/v3/depth?symbol={symbol}&limit={limit}")
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def klines(self, symbol: str, interval: str = "1h", limit: int = 168) -> list:
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url = f"{self.BASE}/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}"
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return self._get(url)
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def exchange_info(self, symbol: str) -> dict:
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data = self._get(f"{self.BASE}/api/v3/exchangeInfo")
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if "symbols" in data:
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for s in data["symbols"]:
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if s.get("symbol") == symbol:
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return s
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return {}
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def funding_rate(self, symbol: str, limit: int = 20) -> list:
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fapi_symbol = symbol.replace("USDT", "-USDT")
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return self._get(f"{self.FAPI}/fapi/v1/fundingRate?symbol={fapi_symbol}&limit={limit}")
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def open_interest(self, symbol: str) -> dict:
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fapi_symbol = symbol.replace("USDT", "-USDT")
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return self._get(f"{self.FAPI}/fapi/v1/openInterest?symbol={fapi_symbol}")
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def ticker_24h_perp(self, symbol: str) -> dict:
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fapi_symbol = symbol.replace("USDT", "-USDT")
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return self._get(f"{self.FAPI}/fapi/v1/ticker/24hr?symbol={fapi_symbol}")
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# ==============================================================================
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# Statistical computation helpers
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# ==============================================================================
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def _compute_annualized_vol(klines: list) -> float:
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"""Compute annualized volatility from hourly klines."""
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if not klines or len(klines) < 10:
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return 80.0 # default mid-cap
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returns = []
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for i in range(1, len(klines)):
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o = float(klines[i][1])
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c = float(klines[i][4])
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if o > 0:
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returns.append(math.log(c / o))
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if len(returns) < 5:
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return 80.0
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mean_r = sum(returns) / len(returns)
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var_r = sum((r - mean_r) ** 2 for r in returns) / (len(returns) - 1)
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hourly_vol = math.sqrt(var_r)
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return hourly_vol * math.sqrt(8760) * 100 # annualize (8760 hours/year)
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def _compute_spread_bps(depth_data: dict) -> float:
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"""Compute spread in bps from depth snapshot."""
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bids = depth_data.get("bids", [])
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asks = depth_data.get("asks", [])
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if not bids or not asks:
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return 5.0
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best_bid = float(bids[0][0])
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best_ask = float(asks[0][0])
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mid = (best_bid + best_ask) / 2
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if mid <= 0:
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return 5.0
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return ((best_ask - best_bid) / mid) * 10000
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def _compute_depth_profile(depth_data: dict, mid_price: float) -> Tuple[float, float]:
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"""Fit depth amplitude and alpha from depth snapshot.
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Returns (amplitude_usd, alpha)."""
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bids = depth_data.get("bids", [])
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asks = depth_data.get("asks", [])
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if not bids or not asks or mid_price <= 0:
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return 50_000.0, 0.9
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cumulative_usd = 0.0
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for level in bids[:50]:
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price = float(level[0])
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qty = float(level[1])
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dist_bps = abs(price - mid_price) / mid_price * 10000
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if dist_bps < 1:
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cumulative_usd += qty * price
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amplitude = max(cumulative_usd, 1_000)
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bid_depths = []
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for level in bids[:50]:
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price = float(level[0])
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qty = float(level[1])
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dist_bps = max(abs(price - mid_price) / mid_price * 10000, 0.5)
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bid_depths.append((dist_bps, qty * price))
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if len(bid_depths) < 5:
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return amplitude, 0.9
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log_dists = [math.log(d) for d, _ in bid_depths if d > 0]
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log_depths = [math.log(max(v, 1)) for d, v in bid_depths if d > 0]
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if len(log_dists) < 5:
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return amplitude, 0.9
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n = len(log_dists)
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sum_x = sum(log_dists)
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sum_y = sum(log_depths)
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sum_xy = sum(x * y for x, y in zip(log_dists, log_depths))
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sum_x2 = sum(x * x for x in log_dists)
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denom = n * sum_x2 - sum_x * sum_x
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if abs(denom) < 1e-10:
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return amplitude, 0.9
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slope = (n * sum_xy - sum_x * sum_y) / denom
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alpha = max(0.5, min(1.5, -slope + 1.0))
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return amplitude, alpha
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def _compute_order_flow_stats(klines: list) -> dict:
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"""Compute order flow statistics from klines."""
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if not klines or len(klines) < 10:
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return {"median_usd": 500, "p99_usd": 100_000, "avg_usd": 2_000}
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volumes_usd = []
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for k in klines:
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vol = float(k[5]) # quote volume
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trades = float(k[8]) # number of trades
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if trades > 0:
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volumes_usd.append(vol / trades)
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if not volumes_usd:
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return {"median_usd": 500, "p99_usd": 100_000, "avg_usd": 2_000}
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volumes_usd.sort()
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n = len(volumes_usd)
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median = volumes_usd[n // 2]
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p99_idx = min(int(n * 0.99), n - 1)
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avg = sum(volumes_usd) / n
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return {"median_usd": median, "p99_usd": volumes_usd[p99_idx], "avg_usd": avg}
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def _compute_funding_stats(funding_data: list) -> Tuple[float, float, float]:
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"""Compute funding rate statistics. Returns (mean_bps, std_bps, positive_pct)."""
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if not funding_data or isinstance(funding_data, dict):
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return 0.10, 0.20, 60.0
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rates = []
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for entry in funding_data:
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r = float(entry.get("fundingRate", 0))
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rates.append(r * 10000) # convert to bps
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if not rates:
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return 0.10, 0.20, 60.0
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mean_r = sum(rates) / len(rates)
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var_r = sum((r - mean_r) ** 2 for r in rates) / max(len(rates) - 1, 1)
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std_r = math.sqrt(var_r)
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pos_pct = sum(1 for r in rates if r > 0) / len(rates) * 100
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return mean_r, std_r, pos_pct
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def _classify_market_cap(mcap_usd: float) -> MarketCapTier:
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if mcap_usd > 500e9:
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return MarketCapTier.MEGA
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if mcap_usd > 50e9:
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return MarketCapTier.LARGE
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if mcap_usd > 5e9:
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return MarketCapTier.MID
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if mcap_usd > 500e6:
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return MarketCapTier.SMALL
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return MarketCapTier.MICRO
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def _classify_volatility(ann_vol: float) -> VolatilityProfile:
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if ann_vol < 30:
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return VolatilityProfile.LOW
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if ann_vol < 80:
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return VolatilityProfile.MEDIUM
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if ann_vol < 150:
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return VolatilityProfile.HIGH
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return VolatilityProfile.EXTREME
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def _classify_liquidity(vol_usd: float) -> LiquidityProfile:
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if vol_usd > 100e6:
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return LiquidityProfile.DEEP
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if vol_usd > 10e6:
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return LiquidityProfile.NORMAL
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if vol_usd > 1e6:
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return LiquidityProfile.THIN
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return LiquidityProfile.ILLIQUID
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# ==============================================================================
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# Heuristic asset classification (for unknown assets)
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# ==============================================================================
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_KNOWN_CLASSIFICATIONS: Dict[str, dict] = {
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"BTCUSDT": {"sector": "currency", "role": "store_of_value", "supply": "fixed_cap",
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"consensus": "pow", "sc": "none", "deriv": "perps_and_options"},
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"ETHUSDT": {"sector": "layer1", "role": "gas", "supply": "disinflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_and_options"},
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"SOLUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"DOGEUSDT": {"sector": "meme", "role": "meme", "supply": "inflationary",
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"consensus": "pow", "sc": "none", "deriv": "perps_only"},
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"ADAUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "dpos", "sc": "full", "deriv": "perps_only"},
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"AVAXUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"UNIUSDT": {"sector": "defi", "role": "governance", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"LINKUSDT": {"sector": "oracle", "role": "utility", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"BNBUSDT": {"sector": "exchange", "role": "exchange_fee", "supply": "burn_mechanism",
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"consensus": "dpos", "sc": "partial", "deriv": "perps_only"},
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"MATICUSDT": {"sector": "layer2", "role": "gas", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"AAVEUSDT": {"sector": "defi", "role": "governance", "supply": "fixed_cap",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"DOTUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "dpos", "sc": "full", "deriv": "perps_only"},
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"ATOMUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"XRPUSDT": {"sector": "currency", "role": "utility", "supply": "inflationary",
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"consensus": "bft", "sc": "partial", "deriv": "perps_only"},
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"TRXUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "dpos", "sc": "full", "deriv": "perps_only"},
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"LTCUSDT": {"sector": "currency", "role": "store_of_value", "supply": "fixed_cap",
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"consensus": "pow", "sc": "none", "deriv": "perps_only"},
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"NEARUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"APTUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "bft", "sc": "full", "deriv": "perps_only"},
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"OPUSDT": {"sector": "layer2", "role": "gas", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"ARBUSDT": {"sector": "layer2", "role": "gas", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"SUIUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "bft", "sc": "full", "deriv": "perps_only"},
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"PEPEUSDT": {"sector": "meme", "role": "meme", "supply": "fixed_cap",
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"consensus": "pos", "sc": "none", "deriv": "perps_only"},
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"WIFUSDT": {"sector": "meme", "role": "meme", "supply": "inflationary",
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"consensus": "pos", "sc": "none", "deriv": "perps_only"},
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"SEIUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "bft", "sc": "full", "deriv": "perps_only"},
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"INJUSDT": {"sector": "defi", "role": "utility", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"FILUSDT": {"sector": "storage", "role": "utility", "supply": "inflationary",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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"HBARUSDT": {"sector": "layer1", "role": "gas", "supply": "inflationary",
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"consensus": "bft", "sc": "full", "deriv": "perps_only"},
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"IMXUSDT": {"sector": "gaming_nft", "role": "utility", "supply": "fixed_cap",
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"consensus": "pos", "sc": "full", "deriv": "perps_only"},
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}
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# ==============================================================================
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# Compile result
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# ==============================================================================
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@dataclass
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class CompileResult:
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symbol: str
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asset_profile: Optional[AssetProfile]
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asset_behavior: Optional[AssetBehavior]
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raw_data: dict
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warnings: List[str] = field(default_factory=list)
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# ==============================================================================
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# Asset Compiler
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# ==============================================================================
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class AssetCompiler:
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"""Auto-fetch from Binance/BingX, compute params, produce profiles."""
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def __init__(self) -> None:
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self.client = BinanceClient()
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def compile(self, symbol: str) -> CompileResult:
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"""Compile a single asset from live API data."""
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warnings = []
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raw = {}
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ticker = self.client.ticker_24h(symbol)
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if "error" in ticker or "lastPrice" not in ticker:
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return CompileResult(symbol=symbol, asset_profile=None, asset_behavior=None,
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raw_data=ticker, warnings=[f"Failed to fetch ticker: {ticker}"])
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price = float(ticker["lastPrice"])
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vol_usd_24h = float(ticker.get("quoteVolume", 0))
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raw["ticker"] = {"price": price, "vol_usd_24h": vol_usd_24h}
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depth_data = self.client.depth(symbol, limit=100)
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spread_bps = _compute_spread_bps(depth_data)
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depth_amp, depth_alpha = _compute_depth_profile(depth_data, price)
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raw["depth"] = {"spread_bps": spread_bps, "amplitude": depth_amp, "alpha": depth_alpha}
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klines = self.client.klines(symbol, "1h", 168)
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ann_vol = _compute_annualized_vol(klines) if klines else 80.0
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flow = _compute_order_flow_stats(klines)
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raw["volatility"] = {"annualized": ann_vol}
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raw["flow"] = flow
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exch_info = self.client.exchange_info(symbol)
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tick_size = 0.01
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lot_size = 0.01
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price_decimals = 2
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if "filters" in exch_info:
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for f in exch_info["filters"]:
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if f["filterType"] == "PRICE_FILTER":
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tick_size = float(f["tickSize"])
|
||||
price_decimals = max(0, -int(math.log10(tick_size)) if tick_size > 0 else 2)
|
||||
elif f["filterType"] == "LOT_SIZE":
|
||||
lot_size = float(f["stepSize"])
|
||||
raw["exchange"] = {"tick_size": tick_size, "lot_size": lot_size}
|
||||
|
||||
funding_data = self.client.funding_rate(symbol, limit=20)
|
||||
funding_mean, funding_std, funding_pos = _compute_funding_stats(funding_data)
|
||||
raw["funding"] = {"mean_bps": funding_mean, "std_bps": funding_std, "positive_pct": funding_pos}
|
||||
|
||||
oi_data = self.client.open_interest(symbol)
|
||||
oi_value = float(oi_data.get("openInterest", 0)) * price if "openInterest" in oi_data else 0
|
||||
raw["oi"] = {"value_usd": oi_value}
|
||||
|
||||
klass = _KNOWN_CLASSIFICATIONS.get(symbol, {})
|
||||
if not klass:
|
||||
warnings.append(f"No classification for {symbol} — using defaults")
|
||||
klass = {"sector": "layer1", "role": "gas", "supply": "inflationary",
|
||||
"consensus": "pos", "sc": "full", "deriv": "perps_only"}
|
||||
|
||||
sector_map = {"currency": Sector.CURRENCY, "layer1": Sector.LAYER1,
|
||||
"layer2": Sector.LAYER2, "defi": Sector.DEFI, "oracle": Sector.ORACLE,
|
||||
"exchange": Sector.EXCHANGE, "meme": Sector.MEME, "privacy": Sector.PRIVACY,
|
||||
"storage": Sector.STORAGE, "gaming_nft": Sector.GAMING_NFT}
|
||||
role_map = {"gas": TokenRole.GAS, "store_of_value": TokenRole.STORE_OF_VALUE,
|
||||
"governance": TokenRole.GOVERNANCE, "utility": TokenRole.UTILITY,
|
||||
"meme": TokenRole.MEME, "exchange_fee": TokenRole.EXCHANGE_FEE}
|
||||
supply_map = {"fixed_cap": SupplyModel.FIXED_CAP, "disinflationary": SupplyModel.DISINFLATIONARY,
|
||||
"inflationary": SupplyModel.INFLATIONARY, "burn_mechanism": SupplyModel.BURN_MECHANISM}
|
||||
consensus_map = {"pow": ConsensusFamily.POW, "pos": ConsensusFamily.POS,
|
||||
"dpos": ConsensusFamily.DPOS, "bft": ConsensusFamily.POS}
|
||||
sc_map = {"full": SmartContractCapability.FULL, "partial": SmartContractCapability.PARTIAL,
|
||||
"none": SmartContractCapability.NONE}
|
||||
deriv_map = {"perps_and_options": DerivativeAccess.PERPS_AND_OPTIONS,
|
||||
"perps_only": DerivativeAccess.PERPS_ONLY, "none": DerivativeAccess.NONE}
|
||||
|
||||
mcap_usd = vol_usd_24h * 100 # rough estimate from volume
|
||||
if vol_usd_24h > 1e9:
|
||||
mcap_est = vol_usd_24h * 2
|
||||
elif vol_usd_24h > 100e6:
|
||||
mcap_est = vol_usd_24h * 5
|
||||
else:
|
||||
mcap_est = vol_usd_24h * 10
|
||||
|
||||
asset_profile = AssetProfile(
|
||||
symbol=symbol,
|
||||
sectors=(sector_map.get(klass["sector"], Sector.LAYER1),),
|
||||
token_roles=(role_map.get(klass["role"], TokenRole.GAS),),
|
||||
supply_model=supply_map.get(klass["supply"], SupplyModel.INFLATIONARY),
|
||||
consensus=consensus_map.get(klass["consensus"], ConsensusFamily.POS),
|
||||
smart_contracts=sc_map.get(klass["sc"], SmartContractCapability.FULL),
|
||||
market_cap_tier=_classify_market_cap(mcap_est),
|
||||
volatility_profile=_classify_volatility(ann_vol),
|
||||
liquidity_profile=_classify_liquidity(vol_usd_24h),
|
||||
derivative_access=deriv_map.get(klass["deriv"], DerivativeAccess.PERPS_ONLY),
|
||||
tick_size=tick_size, lot_size=lot_size, price_decimals=price_decimals,
|
||||
maker_fee_bps=-0.2, taker_fee_bps=0.5,
|
||||
typical_spread_bps=spread_bps, typical_depth_usd=depth_amp,
|
||||
typical_daily_volume_usd=vol_usd_24h,
|
||||
has_funding=True,
|
||||
has_options=klass["deriv"] == "perps_and_options",
|
||||
)
|
||||
|
||||
template_name = "mid_cap_l1"
|
||||
if klass["sector"] in ("meme",):
|
||||
template_name = "retail_meme"
|
||||
elif klass["sector"] in ("currency",) and klass["consensus"] == "pow":
|
||||
template_name = "institutional_blue_chip"
|
||||
elif mcap_est > 50e9:
|
||||
template_name = "institutional_blue_chip"
|
||||
|
||||
tmpl = TEMPLATES[template_name]
|
||||
bingx_spread = spread_bps * 5.0 # conservative estimate
|
||||
bingx_depth_ratio = 0.30
|
||||
|
||||
asset_behavior = AssetBehavior.from_template(
|
||||
symbol, tmpl, {
|
||||
"depth": DepthProfile(
|
||||
amplitude_usd=depth_amp, alpha=depth_alpha,
|
||||
fragility_factor=tmpl.depth.fragility_factor,
|
||||
depth_at_10bps_usd=depth_amp * (10 ** (1 - depth_alpha)),
|
||||
depth_at_100bps_usd=depth_amp * (100 ** (1 - depth_alpha)),
|
||||
),
|
||||
"spread": SpreadProfile(normal_bps=spread_bps, stress_multiplier=tmpl.spread.stress_multiplier),
|
||||
"flow": FlowProfile(
|
||||
orders_per_sec_normal=tmpl.flow.orders_per_sec_normal,
|
||||
orders_per_sec_stress=tmpl.flow.orders_per_sec_stress,
|
||||
cancel_fill_ratio=tmpl.flow.cancel_fill_ratio,
|
||||
median_order_usd=flow["median_usd"],
|
||||
p99_order_usd=flow["p99_usd"],
|
||||
avg_trade_usd=flow["avg_usd"],
|
||||
),
|
||||
"vol": BehVol(
|
||||
annualized_normal=ann_vol,
|
||||
annualized_crisis=ann_vol * 2.5,
|
||||
garch_alpha=tmpl.vol.garch_alpha,
|
||||
garch_beta=tmpl.vol.garch_beta,
|
||||
half_life_hours=tmpl.vol.half_life_hours,
|
||||
),
|
||||
"funding": FundingProfile(
|
||||
mean_bps_8h=funding_mean, std_bps_8h=funding_std,
|
||||
positive_pct=funding_pos, basis_typical_bps=abs(funding_mean) * 5,
|
||||
),
|
||||
"bingx": BingxProfile(
|
||||
spread_mult=bingx_spread / max(spread_bps, 0.01),
|
||||
depth_ratio=bingx_depth_ratio,
|
||||
latency_ms=100, taker_fee_bps=5.0, maker_fee_bps=2.0,
|
||||
funding_lag_hours=4,
|
||||
),
|
||||
"liquidation": LiquidationProfile(
|
||||
oi_mcap_ratio=oi_value / max(mcap_est, 1),
|
||||
trigger_pct=tmpl.liquidation.trigger_pct,
|
||||
speed=tmpl.liquidation.speed,
|
||||
recovery=tmpl.liquidation.recovery,
|
||||
),
|
||||
},
|
||||
reference_price=price,
|
||||
)
|
||||
|
||||
return CompileResult(
|
||||
symbol=symbol,
|
||||
asset_profile=asset_profile,
|
||||
asset_behavior=asset_behavior,
|
||||
raw_data=raw,
|
||||
warnings=warnings,
|
||||
)
|
||||
|
||||
def compile_batch(self, symbols: List[str]) -> List[CompileResult]:
|
||||
"""Compile multiple assets sequentially (rate-limited)."""
|
||||
results = []
|
||||
for sym in symbols:
|
||||
results.append(self.compile(sym))
|
||||
return results
|
||||
|
||||
def register(self, result: CompileResult) -> bool:
|
||||
"""Register compiled results into the global registries."""
|
||||
if not result.asset_profile or not result.asset_behavior:
|
||||
return False
|
||||
ASSET_PROFILES[result.symbol] = result.asset_profile
|
||||
ASSET_BEHAVIORS[result.symbol] = result.asset_behavior
|
||||
return True
|
||||
|
||||
def compile_and_register(self, symbol: str) -> CompileResult:
|
||||
"""Compile and register in one step."""
|
||||
result = self.compile(symbol)
|
||||
self.register(result)
|
||||
return result
|
||||
Reference in New Issue
Block a user