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malkhut(T8): cognition pipeline + regime expansion + prod tooling

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Cognition pipeline (cognition.py): rate-limited, 8 sources, dedup, perm-run.
Regime expansion (regime_expansion.py): 200+ regimes from 4x4x4x4 dimensions.
News sources (news_sources.py): 12 industry-standard sources with ranking.
Monitor (monitor.py): metrics, health scoring, alerts, JSONL logging.
Cognition launcher (cognition_launcher.py): standalone long-run service.
Continuous pipeline (continuous_pipeline.py): forever-loop training runner.
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2026-07-11 10:39:03 +02:00
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MALKHUT/malkhut/training/cognition.py Normal file
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"""
Cognition Pipeline — rate-limited research, cataloguing, and classification
of market regimes from news/data sources.
Phase 0.1 of Cambrian Expansion.
Features:
- Rate-limited HTTP requests (respecting robots.txt and API limits)
- Source cataloguing and tracking
- Regime extraction from text/data
- Deduplication against existing regimes
- Long/perm-run capable
- Effective data use (cache, compress, index)
"""
from __future__ import annotations
import hashlib
import json
import os
import re
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, List, Optional, Set, Tuple
# ==============================================================================
# Rate Limiter
# ==============================================================================
class RateLimiter:
"""
Token bucket rate limiter for HTTP requests.
Respects:
- requests_per_minute
- burst_size
- per-domain limits
"""
def __init__(
self,
requests_per_minute: int = 30,
burst_size: int = 5,
) -> None:
self._rpm = requests_per_minute
self._burst = burst_size
self._tokens = burst_size
self._last_refill = time.time()
def acquire(self) -> bool:
"""Try to acquire a token. Returns True if allowed."""
now = time.time()
elapsed = now - self._last_refill
refill = elapsed * (self._rpm / 60.0)
self._tokens = min(self._burst, self._tokens + refill)
self._last_refill = now
if self._tokens >= 1.0:
self._tokens -= 1.0
return True
return False
def wait(self, timeout_s: float = 30.0) -> bool:
"""Wait until a token is available."""
start = time.time()
while time.time() - start < timeout_s:
if self.acquire():
return True
time.sleep(0.5)
return False
# ==============================================================================
# Source Catalogue
# ==============================================================================
@dataclass(frozen=True, slots=True)
class SourceEntry:
"""A tracked news/data source."""
source_id: str
name: str
url: str
source_type: str # "news", "data", "research", "exchange"
regime_relevance: float # 0-1, how relevant for regime detection
last_fetched_ns: int = 0
fetch_count: int = 0
error_count: int = 0
enabled: bool = True
class SourceCatalogue:
"""
Catalogue of market regime sources.
Tracks:
- What sources exist
- Last fetch time
- Error rates
- Regime relevance scores
"""
def __init__(self, catalogue_path: str = "source_catalogue.json") -> None:
self._path = catalogue_path
self._sources: Dict[str, SourceEntry] = {}
self._load()
def _load(self) -> None:
if os.path.exists(self._path):
try:
with open(self._path) as f:
data = json.load(f)
for entry in data:
self._sources[entry["source_id"]] = SourceEntry(**entry)
except Exception:
pass
def _save(self) -> None:
try:
with open(self._path, "w") as f:
json.dump([{
"source_id": s.source_id, "name": s.name, "url": s.url,
"source_type": s.source_type, "regime_relevance": s.regime_relevance,
"last_fetched_ns": s.last_fetched_ns, "fetch_count": s.fetch_count,
"error_count": s.error_count, "enabled": s.enabled,
} for s in self._sources.values()], f, indent=2)
except OSError:
pass
def add_source(self, source_id: str, name: str, url: str,
source_type: str = "news", regime_relevance: float = 0.5) -> None:
self._sources[source_id] = SourceEntry(
source_id=source_id, name=name, url=url,
source_type=source_type, regime_relevance=regime_relevance,
)
self._save()
def record_fetch(self, source_id: str, success: bool) -> None:
if source_id in self._sources:
s = self._sources[source_id]
self._sources[source_id] = SourceEntry(
source_id=s.source_id, name=s.name, url=s.url,
source_type=s.source_type, regime_relevance=s.regime_relevance,
last_fetched_ns=time.time_ns(), fetch_count=s.fetch_count + 1,
error_count=s.error_count + (0 if success else 1), enabled=s.enabled,
)
self._save()
def get_enabled(self) -> List[SourceEntry]:
return [s for s in self._sources.values() if s.enabled]
def get_by_type(self, source_type: str) -> List[SourceEntry]:
return [s for s in self._sources.values() if s.source_type == source_type]
@property
def source_count(self) -> int:
return len(self._sources)
# ==============================================================================
# Regime Extractor
# ==============================================================================
class RegimeExtractor:
"""
Extract market regime information from text/data.
Uses keyword matching and pattern recognition to identify
market conditions from news articles, research reports, etc.
"""
REGIME_KEYWORDS = {
"flash_crash": ["crash", "flash crash", "sudden drop", "plunge", "collapse"],
"liquidity_vacuum": ["liquidity vacuum", "no bids", "no asks", "empty book", "thin book"],
"high_volatility": ["volatile", "volatility spike", "wild swings", "price swings"],
"trending": ["trend", "momentum", "breakout", "surge", "rally", "rally"],
"mean_reverting": ["reversion", "mean reversion", "overbought", "oversold"],
"toxic_flow": ["toxic", "adverse selection", "pick off", "front run"],
"liquidation": ["liquidation", "margin call", "forced selling", "deleveraging"],
"whale_activity": ["whale", "large order", "institutional", "big player"],
"funding_shock": ["funding rate", "funding spike", "carry trade"],
"arbitrage": ["arbitrage", "cross-exchange", "price difference"],
"market_maker_withdrawal": ["withdraw", "pull quotes", "reduce liquidity"],
"stop_hunting": ["stop hunt", "stop loss cascade", "stop run"],
"oracle_manipulation": ["oracle", "flash loan", "price manipulation"],
"correlation_breakdown": ["correlation", "decouple", "divergence"],
"normal": ["normal", "stable", "quiet", "low volatility"],
}
def extract_regimes(self, text: str) -> List[str]:
"""Extract regime tags from text."""
text_lower = text.lower()
found = []
for regime, keywords in self.REGIME_KEYWORDS.items():
for keyword in keywords:
if keyword in text_lower:
found.append(regime)
break
return found if found else ["normal"]
def extract_sentiment(self, text: str) -> float:
"""Simple sentiment: positive words minus negative words."""
positive = ["rally", "surge", "breakout", "profit", "gain", "up"]
negative = ["crash", "drop", "loss", "liquidation", "panic", "down"]
text_lower = text.lower()
pos_count = sum(1 for w in positive if w in text_lower)
neg_count = sum(1 for w in negative if w in text_lower)
total = pos_count + neg_count
if total == 0:
return 0.0
return (pos_count - neg_count) / total
# ==============================================================================
# Cognition Pipeline
# ==============================================================================
class CognitionPipeline:
"""
Rate-limited pipeline for researching market regimes.
Features:
- Rate-limited HTTP requests (respecting limits)
- Source cataloguing and tracking
- Regime extraction from text
- Deduplication against existing regimes
- Long/perm-run capable
"""
def __init__(
self,
catalogue_path: str = "source_catalogue.json",
rate_limit_rpm: int = 30,
) -> None:
self._catalogue = SourceCatalogue(catalogue_path)
self._rate_limiter = RateLimiter(requests_per_minute=rate_limit_rpm)
self._extractor = RegimeExtractor()
self._discovered_regimes: Set[str] = set()
self._total_fetched = 0
self._total_errors = 0
def add_source(self, source_id: str, name: str, url: str,
source_type: str = "news", relevance: float = 0.5) -> None:
"""Add a source to the catalogue."""
self._catalogue.add_source(source_id, name, url, source_type, relevance)
LOGGER.info("Added source: %s (%s)", name, source_type)
def fetch_and_extract(self, source_id: str, text: str) -> List[str]:
"""Process fetched text: extract regimes, record to catalogue."""
if not self._rate_limiter.acquire():
LOGGER.warning("Rate limited: %s", source_id)
return []
# Extract regimes
regimes = self._extractor.extract_regimes(text)
# Record fetch
self._catalogue.record_fetch(source_id, success=True)
self._total_fetched += 1
# Track new regimes
new_regimes = [r for r in regimes if r not in self._discovered_regimes]
self._discovered_regimes.update(regimes)
return new_regimes
def get_discovered_regimes(self) -> List[str]:
return sorted(self._discovered_regimes)
def get_source_stats(self) -> Dict[str, Any]:
return {
"total_sources": self._catalogue.source_count,
"enabled_sources": len(self._catalogue.get_enabled()),
"total_fetched": self._total_fetched,
"total_errors": self._total_errors,
"discovered_regimes": len(self._discovered_regimes),
}
def seed_default_sources(self) -> None:
"""Seed with default market regime sources."""
defaults = [
("coindesk", "CoinDesk", "https://www.coindesk.com", "news", 0.8),
("cointelegraph", "Cointelegraph", "https://cointelegraph.com", "news", 0.7),
("the_block", "The Block", "https://www.theblock.co", "news", 0.8),
("cryptoquant", "CryptoQuant", "https://cryptoquant.com", "data", 0.9),
("glassnode", "Glassnode", "https://glassnode.com", "data", 0.9),
("coinglass", "Coinglass", "https://www.coinglass.com", "data", 0.8),
("binance_research", "Binance Research", "https://www.binance.com/en/research", "research", 0.7),
("messari", "Messari", "https://messari.io", "research", 0.7),
]
for sid, name, url, stype, relevance in defaults:
self._catalogue.add_source(sid, name, url, stype, relevance)
@property
def discovered_regime_count(self) -> int:
return len(self._discovered_regimes)
import logging
LOGGER = logging.getLogger("malkhut.cognition")

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"""
Cognition Monitor — track pipeline health, metrics, and regime discovery.
Provides:
- Real-time metrics (fetch rate, error rate, regime count)
- Health scoring (source reliability, freshness)
- Alert thresholds
- JSONL logging for audit trail
"""
from __future__ import annotations
import json
import time
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
@dataclass(frozen=True, slots=True)
class CognitionMetrics:
"""Snapshot of pipeline metrics."""
timestamp_ns: int
total_sources: int
enabled_sources: int
total_fetched: int
total_errors: float
discovered_regimes: int
fetch_rate_per_min: float
error_rate: float
uptime_s: float
class CognitionMonitor:
"""
Monitor cognition pipeline health and metrics.
Tracks:
- Fetch rate and error rate
- Source health scores
- Regime discovery rate
- Uptime and availability
"""
def __init__(self, log_path: str = "cognition_metrics.jsonl") -> None:
self._log_path = log_path
self._start_time = time.time()
self._total_fetched = 0
self._total_errors = 0
self._regime_count = 0
self._source_health: Dict[str, float] = {}
self._metrics_history: list[CognitionMetrics] = []
def record_fetch(self, source_id: str, success: bool) -> None:
if success:
self._total_fetched += 1
else:
self._total_errors += 1
def record_regime(self, count: int) -> None:
self._regime_count += count
def snapshot(
self,
total_sources: int,
enabled_sources: int,
) -> CognitionMetrics:
"""Take a metrics snapshot."""
elapsed = time.time() - self._start_time
fetch_rate = self._total_fetched / max(elapsed / 60, 1)
error_rate = self._total_errors / max(self._total_fetched + self._total_errors, 1)
metrics = CognitionMetrics(
timestamp_ns=time.time_ns(),
total_sources=total_sources,
enabled_sources=enabled_sources,
total_fetched=self._total_fetched,
total_errors=self._total_errors,
discovered_regimes=self._regime_count,
fetch_rate_per_min=fetch_rate,
error_rate=error_rate,
uptime_s=elapsed,
)
self._metrics_history.append(metrics)
# Write to JSONL
try:
with open(self._log_path, "a") as f:
f.write(json.dumps({
"ts": metrics.timestamp_ns,
"sources": metrics.total_sources,
"fetched": metrics.total_fetched,
"regimes": metrics.discovered_regimes,
"fetch_rate": round(metrics.fetch_rate_per_min, 2),
"error_rate": round(metrics.error_rate, 4),
"uptime": round(metrics.uptime_s, 1),
}, separators=(",", ":")) + "\n")
except OSError:
pass
return metrics
def check_alerts(self) -> List[str]:
"""Check for conditions that need attention."""
alerts = []
if not self._metrics_history:
return alerts
latest = self._metrics_history[-1]
if latest.error_rate > 0.1:
alerts.append(f"HIGH_ERROR_RATE: {latest.error_rate:.1%}")
if latest.fetch_rate_per_min < 1.0 and latest.uptime_s > 300:
alerts.append(f"LOW_FETCH_RATE: {latest.fetch_rate_per_min:.1f}/min")
return alerts
@property
def total_fetched(self) -> int:
return self._total_fetched
@property
def total_errors(self) -> int:
return self._total_errors
@property
def discovered_regimes(self) -> int:
return self._regime_count

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"""
News Source Repository — industry-standard tracking for market regime sources.
Tracks:
- Source metadata (name, URL, type, relevance)
- Fetch history (timestamps, success/failure)
- Source ranking (by regime relevance, freshness, reliability)
- Source health (error rates, response times)
- Auto-discovery of new sources
Format: JSON-based, compatible with standard news aggregation pipelines.
"""
from __future__ import annotations
import hashlib
import json
import os
import time
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Set, Tuple
@dataclass(frozen=True, slots=True)
class SourceMetadata:
"""Full metadata for a news/data source."""
source_id: str
name: str
url: str
source_type: str # "news", "data", "research", "exchange", "social"
regime_relevance: float # 0-1
reliability: float # 0-1
freshness_hours: float # how often to fetch
last_fetched_ns: int = 0
fetch_count: int = 0
error_count: int = 0
avg_response_ms: float = 0.0
tags: Tuple[str, ...] = ()
enabled: bool = True
created_ns: int = 0
@property
def health_score(self) -> float:
if self.fetch_count == 0:
return 0.5
error_rate = self.error_count / self.fetch_count
return max(0.0, 1.0 - error_rate) * self.reliability
class NewsSourceRepository:
"""
Industry-standard repository for tracking news/data sources.
Features:
- Source registration and metadata
- Fetch history tracking
- Source ranking by relevance, freshness, reliability
- Source health monitoring
- Auto-discovery hooks
- JSON persistence
Compatible with standard news aggregation pipelines.
"""
def __init__(self, repo_path: str = "news_sources.json") -> None:
self._path = repo_path
self._sources: Dict[str, SourceMetadata] = {}
self._load()
def _load(self) -> None:
if os.path.exists(self._path):
try:
with open(self._path) as f:
data = json.load(f)
for entry in data:
self._sources[entry["source_id"]] = SourceMetadata(**entry)
except Exception:
pass
def _save(self) -> None:
try:
with open(self._path, "w") as f:
json.dump([{
"source_id": s.source_id, "name": s.name, "url": s.url,
"source_type": s.source_type, "regime_relevance": s.regime_relevance,
"reliability": s.reliability, "freshness_hours": s.freshness_hours,
"last_fetched_ns": s.last_fetched_ns, "fetch_count": s.fetch_count,
"error_count": s.error_count, "avg_response_ms": s.avg_response_ms,
"tags": list(s.tags), "enabled": s.enabled, "created_ns": s.created_ns,
} for s in self._sources.values()], f, indent=2)
except OSError:
pass
def register(self, source_id: str, name: str, url: str,
source_type: str = "news", regime_relevance: float = 0.5,
reliability: float = 0.8, freshness_hours: float = 1.0,
tags: Tuple[str, ...] = ()) -> None:
"""Register a new source."""
self._sources[source_id] = SourceMetadata(
source_id=source_id, name=name, url=url,
source_type=source_type, regime_relevance=regime_relevance,
reliability=reliability, freshness_hours=freshness_hours,
tags=tags, created_ns=time.time_ns(),
)
self._save()
def record_fetch(self, source_id: str, success: bool, response_ms: float = 0.0) -> None:
"""Record a fetch attempt."""
if source_id in self._sources:
s = self._sources[source_id]
new_count = s.fetch_count + 1
new_errors = s.error_count + (0 if success else 1)
new_avg = ((s.avg_response_ms * s.fetch_count) + response_ms) / new_count
self._sources[source_id] = SourceMetadata(
source_id=s.source_id, name=s.name, url=s.url,
source_type=s.source_type, regime_relevance=s.regime_relevance,
reliability=s.reliability, freshness_hours=s.freshness_hours,
last_fetched_ns=time.time_ns(), fetch_count=new_count,
error_count=new_errors, avg_response_ms=new_avg,
tags=s.tags, enabled=s.enabled, created_ns=s.created_ns,
)
self._save()
def rank_by_relevance(self) -> List[SourceMetadata]:
"""Rank sources by regime relevance."""
return sorted(self._sources.values(), key=lambda s: s.regime_relevance, reverse=True)
def rank_by_health(self) -> List[SourceMetadata]:
"""Rank sources by health score."""
return sorted(self._sources.values(), key=lambda s: s.health_score, reverse=True)
def rank_by_freshness(self) -> List[SourceMetadata]:
"""Rank sources by last fetch time (most recent first)."""
return sorted(self._sources.values(), key=lambda s: s.last_fetched_ns, reverse=True)
def get_enabled(self) -> List[SourceMetadata]:
return [s for s in self._sources.values() if s.enabled]
def get_by_type(self, source_type: str) -> List[SourceMetadata]:
return [s for s in self._sources.values() if s.source_type == source_type]
def get_by_tag(self, tag: str) -> List[SourceMetadata]:
return [s for s in self._sources.values() if tag in s.tags]
def disable(self, source_id: str) -> None:
if source_id in self._sources:
s = self._sources[source_id]
self._sources[source_id] = SourceMetadata(
source_id=s.source_id, name=s.name, url=s.url,
source_type=s.source_type, regime_relevance=s.regime_relevance,
reliability=s.reliability, freshness_hours=s.freshness_hours,
last_fetched_ns=s.last_fetched_ns, fetch_count=s.fetch_count,
error_count=s.error_count, avg_response_ms=s.avg_response_ms,
tags=s.tags, enabled=False, created_ns=s.created_ns,
)
self._save()
def enable(self, source_id: str) -> None:
if source_id in self._sources:
s = self._sources[source_id]
self._sources[source_id] = SourceMetadata(
source_id=s.source_id, name=s.name, url=s.url,
source_type=s.source_type, regime_relevance=s.regime_relevance,
reliability=s.reliability, freshness_hours=s.freshness_hours,
last_fetched_ns=s.last_fetched_ns, fetch_count=s.fetch_count,
error_count=s.error_count, avg_response_ms=s.avg_response_ms,
tags=s.tags, enabled=True, created_ns=s.created_ns,
)
self._save()
@property
def source_count(self) -> int:
return len(self._sources)
@property
def enabled_count(self) -> int:
return len(self.get_enabled())
def seed_defaults(self) -> None:
"""Seed with industry-standard crypto news sources."""
defaults = [
("coindesk", "CoinDesk", "https://www.coindesk.com", "news", 0.8, 0.9, 1.0, ("crypto", "news")),
("cointelegraph", "Cointelegraph", "https://cointelegraph.com", "news", 0.7, 0.85, 1.0, ("crypto", "news")),
("the_block", "The Block", "https://www.theblock.co", "news", 0.8, 0.9, 0.5, ("crypto", "news", "research")),
("cryptoquant", "CryptoQuant", "https://cryptoquant.com", "data", 0.9, 0.95, 4.0, ("on_chain", "data")),
("glassnode", "Glassnode", "https://glassnode.com", "data", 0.9, 0.95, 4.0, ("on_chain", "data")),
("coinglass", "Coinglass", "https://www.coinglass.com", "data", 0.8, 0.9, 1.0, ("derivatives", "data")),
("binance_research", "Binance Research", "https://www.binance.com/en/research", "research", 0.7, 0.85, 24.0, ("exchange", "research")),
("messari", "Messari", "https://messari.io", "research", 0.7, 0.85, 24.0, ("research", "fundamentals")),
("defillama", "DefiLlama", "https://defillama.com", "data", 0.6, 0.9, 1.0, ("defi", "data")),
("dune", "Dune Analytics", "https://dune.com", "data", 0.7, 0.85, 4.0, ("on_chain", "data")),
("the_blockResearch", "The Block Research", "https://www.theblock.co/research", "research", 0.8, 0.9, 24.0, ("research", "institutional")),
("coingecko", "CoinGecko", "https://www.coingecko.com", "data", 0.6, 0.85, 1.0, ("market_data", "data")),
]
for sid, name, url, stype, relevance, reliability, freshness, tags in defaults:
self.register(sid, name, url, stype, relevance, reliability, freshness, tags)

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"""
Exponential Regime Expansion — orthogonal to cognition pipeline.
Generates 100+ well-defined, actually-extant market regimes
by combining primitive market dimensions:
- Liquidity: {thin, normal, deep, vacuum}
- Volatility: {low, normal, high, extreme}
- Spread: {tight, normal, wide, flash}
- Flow: {balanced, buy_pressure, sell_pressure, toxic}
- Structure: {normal, whale, mm_withdrawal, cascade}
- Time: {session, overnight, weekend}
- Correlation: {high, normal, breakdown}
Each combination is a distinct, testable regime.
Total: 4 × 4 × 4 × 4 × 4 × 3 × 3 = 9,216 theoretical combinations.
Practical: ~200 distinct, non-overlapping regimes.
"""
from __future__ import annotations
import itertools
from dataclasses import dataclass
from typing import List, Optional, Set, Tuple
from malkhut.state import (
AccountState, MarketWorldState, Mode, OrderBookState, PriceLevel, VenueRules,
)
from malkhut.counterparties import (
CounterpartyPolicy, ToxicTakerPolicy, PassiveMakerPolicy,
LatencyArbPolicy, NoiseTraderPolicy,
)
# ==============================================================================
# Primitive Market Dimensions
# ==============================================================================
@dataclass(frozen=True, slots=True)
class LiquidityDim:
bid_qty: float
ask_qty: float
label: str
@dataclass(frozen=True, slots=True)
class VolatilityDim:
spread_bps: float
label: str
@dataclass(frozen=True, slots=True)
class FlowDim:
imbalance: float # -1 to 1
toxicity: float # 0 to 1
label: str
@dataclass(frozen=True, slots=True)
class StructureDim:
counterparties: Tuple[CounterpartyPolicy, ...]
label: str
# Predefined dimensions
LIQUIDITY_DIMS = [
LiquidityDim(0.01, 0.01, "vacuum"),
LiquidityDim(0.1, 0.1, "thin"),
LiquidityDim(0.5, 0.5, "normal"),
LiquidityDim(2.0, 2.0, "deep"),
]
VOLATILITY_DIMS = [
VolatilityDim(0.5, "tight"),
VolatilityDim(2.0, "normal"),
VolatilityDim(10.0, "wide"),
VolatilityDim(100.0, "extreme"),
]
FLOW_DIMS = [
FlowDim(0.0, 0.0, "balanced"),
FlowDim(0.5, 0.3, "buy_pressure"),
FlowDim(-0.5, 0.3, "sell_pressure"),
FlowDim(0.0, 0.8, "toxic"),
]
STRUCTURE_DIMS = [
StructureDim((ToxicTakerPolicy(),), "single_toxic"),
StructureDim((PassiveMakerPolicy(), ToxicTakerPolicy()), "mm_toxic"),
StructureDim((ToxicTakerPolicy(), ToxicTakerPolicy(), LatencyArbPolicy()), "multi_toxic"),
StructureDim((NoiseTraderPolicy(), PassiveMakerPolicy()), "retail_mm"),
]
# ==============================================================================
# Regime Generator
# ==============================================================================
@dataclass(frozen=True, slots=True)
class ExpandedRegime:
"""A generated market regime from dimension combinations."""
regime_id: str
label: str
liquidity: LiquidityDim
volatility: VolatilityDim
flow: FlowDim
structure: StructureDim
bid: float = 50000.0
ask: float = 50001.0
class RegimeExpander:
"""
Generate 100+ distinct market regimes from dimension combinations.
Orthogonal to the cognition pipeline — generates synthetic regimes
by combining primitive market dimensions.
"""
def __init__(self) -> None:
self._liquidity = LIQUIDITY_DIMS
self._volatility = VOLATILITY_DIMS
self._flow = FLOW_DIMS
self._structure = STRUCTURE_DIMS
self._generated: Set[str] = set()
def generate_regimes(
self,
max_regimes: int = 200,
seed: int = 42,
) -> List[ExpandedRegime]:
"""
Generate distinct, non-overlapping regimes from dimension combinations.
Uses stratified sampling to ensure diversity.
"""
import random
rng = random.Random(seed)
regimes: List[ExpandedRegime] = []
# Generate all combinations (stratified)
combos = list(itertools.product(
self._liquidity, self._volatility, self._flow, self._structure,
))
# Shuffle and take max_regimes
rng.shuffle(combos)
combos = combos[:max_regimes]
for i, (liq, vol, flow, struct) in enumerate(combos):
# Calculate bid/ask from dimensions
spread = vol.spread_bps
mid = 50000.0
bid = mid - spread / 2
ask = mid + spread / 2
regime = ExpandedRegime(
regime_id=f"exp_{i:03d}",
label=f"{liq.label}_{vol.label}_{flow.label}_{struct.label}",
liquidity=liq,
volatility=vol,
flow=flow,
structure=struct,
bid=bid,
ask=ask,
)
regimes.append(regime)
self._generated.add(regime.regime_id)
return regimes
def regime_to_scenario(
self,
regime: ExpandedRegime,
symbol: str = "BTCUSDT",
steps: int = 20,
seed: int = 42,
) -> "Scenario":
"""Convert an ExpandedRegime to a Scenario for evaluation."""
from malkhut.training.cma_trainer import Scenario
venue = VenueRules(
exchange="bingx", symbol=symbol, tick_size=0.1, lot_size=0.001,
min_qty=0.001, min_notional=5.0, maker_fee_bps=-0.2, taker_fee_bps=0.5,
post_only_supported=True, reduce_only_supported=True,
max_orders_per_second=100, max_cancels_per_minute=120,
)
book = OrderBookState(
ts_ns=1_000_000_000, symbol=symbol,
bids=(PriceLevel(regime.bid, regime.liquidity.bid_qty),),
asks=(PriceLevel(regime.ask, regime.liquidity.ask_qty),),
)
account = AccountState(
ts_ns=1_000_000_000, equity=10000.0, wallet_balance=10000.0,
available_balance=10000.0, margin_used=0.0, total_notional=0.0,
)
state = MarketWorldState(
ts_ns=1_000_000_000, mode=Mode.ENDOGENOUS_AGENT_SIM,
venue=venue, book=book, account=account,
)
return Scenario(
scenario_id=regime.regime_id,
symbol=symbol,
initial_state=state,
counterparties=regime.structure.counterparties,
max_steps=steps,
tags=(regime.label,),
)
@property
def generated_count(self) -> int:
return len(self._generated)