Codex
ebf772fe5a
malkhut(docs): cross-exchange learning + adversary ecology documented
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README updated with:
- Cross-exchange learning: ScenarioFactory exchange_id + cross_exchange_transfer
- PerformanceMatrix keyed by (regime, strategy_id, venue)
- Adversary ecology: ActionKind-level abstraction, venue-independent
- Transferability principle: parameters transfer, names are venue-specific
2026-07-14 15:44:33 +02:00
Codex
455a7a5a4e
malkhut(docs): README updated for three-dimensional order type model
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Updated README to reflect Fable's corrections:
- OrderType/TimeInForce/Instructions as three orthogonal dimensions
- POST_ONLY/IOC/FOK correctly described as non-types
- BingX trailing_stop -> TRAILING_STOP_MARKET
- Three mapping tables (order type, TIF, instructions)
- Integration status updated
2026-07-14 14:52:50 +02:00
Codex
f48af7c405
malkhut(docs): OrderType integration status documented
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README updated with:
- Integration Status section for OrderType
- state.py: 17 values, backward compatible
- order_types.py: standalone standardized taxonomy
- ExchangeProfile.available_order_types per venue
- CWM transition: passes action.order_type to venue adapter
- Agent/adversary: check is_type_available before placing
2026-07-14 13:11:19 +02:00
Codex
53e02c84ec
malkhut: standardized order types — FIX/CCXT-aligned, multi-exchange mapping
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order_types.py: Five-layer taxonomy normalized to industry standards:
Layer 1: Base types (FIX Tag 40) — MARKET, LIMIT
Layer 2: Time-in-force (FIX Tag 59) — GTC, IOC, FOK, GTD
Layer 3: Conditional/Trigger (FIX Tag 3/4+MIT) — STOP_MARKET, STOP_LIMIT,
TRIGGER_MARKET, TRIGGER_LIMIT, TRAILING_STOP
Layer 4: Instructions (FIX Tag 18) — POST_ONLY, REDUCE_ONLY, HIDDEN, ICEBERG
Layer 5: Compound (exchange-specific) — OCO, TP_SL
Cross-exchange mapping: BingX ↔ Binance ↔ Bybit (from CCXT source code).
Standards: FIX 4.4 Tag 40/59/18, CCXT unified API, ISO 10383 (MIC).
Transferability: strategy PARAMETERS transfer. ORDER TYPE NAMES are
venue-specific but semantics identical (LIMIT = LIMIT everywhere).
14 tests. README updated with full mapping table and standards references.
2026-07-14 12:02:01 +02:00
Codex
13811cc789
malkhut(docs): comprehensive update — all 10 Fable spec items documented
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README updated with:
- Fable spec items table (10 items, status)
- New subsystems: ScenarioLibrary, ManifoldQuery, ActualsLoader, BookFidelity, DAAT, OOD
- Package structure: daat/ directory added
- All modules documented with test counts
25 commits total. 1204 tests. All green.
2026-07-14 09:14:31 +02:00
Codex
84a94a7098
malkhut(docs): fee correction + Fable spec status + 5000-eval results
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README updated with:
- Fee correction table (10x bug fixed, source of truth documented)
- All prior policies flagged as suspect at correct fees
- 5000-eval partial results: best=15,080 at gen 105, still climbing
- Fable's spec (SPEC_MALKHUT_ACTUALS_INTAKE.md) acknowledged:
- Fee bug fixed (commit 5523be1d
2026-07-13 21:50:57 +02:00
Codex
70964394d4
malkhut(docs + bench): comprehensive update + smoke test script
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README updated with:
- Vectorized UCB selection (7.7x speedup, 1.13µs/selection)
- Batch MCTS kernel (numba-accelerated)
- Fast scalar + advantage scoring modes
- Updated performance benchmarks (1186 tests, 390 scenarios, 3043 score/min)
- Advantage scorer module in package structure
smoke_1h.py: standalone training script for extended runs.
Total session: 19 commits, 1186 tests, all green.
All implementations: parallel eval (7x), vectorized reward (numba),
vectorized UCB (7.7x), fast scalar scoring, advantage mode,
DuckDB store (sub-µs reads), asset compiler, behavior DSL,
multi-exchange support, three-layer identifiers.
2026-07-13 17:04:40 +02:00
Codex
e49b959b2e
malkhut(docs): update with parallel training benchmarks + optimizations
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- Training Scaling: 7x speedup at 8 workers, 87% efficiency, 1718 score/min
- Vectorized reward: numba JIT bypasses FeatureVector dict allocation
- DuckDB: sub-µs reads via in-memory materialization
- VBT analysis: post-sim trade metrics (Sharpe, Sortino, VaR)
- CMA-ES now wires workers into training loop via CMAESTrainer(workers=N)
- Updated all subsystem tables, test counts, performance benchmarks
2026-07-13 09:27:21 +02:00
Codex
9fe989b502
malkhut(perf): DuckDB in-memory materialization — sub-µs reads, zero DuckDB overhead
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Architecture: DuckDB for persistence + full in-memory materialization for reads.
All reads served from Python dicts (sub-microsecond). DuckDB only hit on writes.
Performance evolution (get_asset benchmark):
V0 (raw DuckDB): 876µs per call
V1 (LRU cache): 2.3µs per call (380x)
V2 (in-memory): 0.2µs per call (4380x)
All reads now sub-microsecond:
get_asset: 0.2µs (was 876µs)
query(blockian): 6.6µs (was 2.2ms)
query(sector): 6.6µs (was 3.2ms)
exchange lookup: 12.5µs (was 1.5ms)
full scan: 5.9µs (was 1.8ms)
behavior: 0.4µs
Write path: sync_from_profiles batch-inserts all data, then materializes
into Python dicts. Resync: 76ms (was 210ms, 2.8x faster).
Data integrity: DuckDB WAL provides crash recovery. In-memory dicts are
reconstructed from DB on every sync/close-reopen cycle. Zero data loss.
2026-07-12 19:38:24 +02:00
Codex
7f27ed22c8
malkhut: DuckDB file-backed asset store — schema, sync, queries, persistence
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DuckDB store (asset_store.py):
- 4 tables: assets (28 cols), exchanges (10 cols), asset_exchanges (junction),
behavior_profiles (28 cols)
- Schema with indexes on blockchain, coingecko_id, cmc_id, exchange_id
- sync_from_profiles(): populate from in-memory dicts in one call
- Query API: query_assets(**filters), get_asset(), symbols_for_exchange(),
assets_on_blockchain(), asset_count(), exchange_count()
- Case-insensitive exchange lookup
- File-backed persistence: data survives connection close/reopen
- Performance: <1s sync, 100 queries in <1s, 1000 gets in <1s
30 tests covering:
- Schema creation and table structure (3 tests)
- Sync from profiles (6 tests, idempotent)
- Asset CRUD (7 tests, query by blockchain/coingecko/sector)
- Exchange mapping (5 tests, case-insensitive)
- Behavior profiles (4 tests)
- Persistence across connections (2 tests)
- Performance baseline (3 tests)
Total: 1276 tests across 50 files, all green, zero regressions.
2026-07-12 12:21:48 +02:00
Codex
1f709af6d1
malkhut: three-layer identifier architecture for cross-system asset identification
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Layer 1 (canonical identity): symbol, base_asset, name, unified_symbol
(CCXT format), quote_currency
Layer 2 (cross-system): coingecko_id, cmc_id, blockchain, contract_address
Layer 3 (exchange mapping): exchanges tuple
All 13 pre-defined assets migrated with accurate CoinGecko IDs, CMC IDs,
blockchains, and contract addresses (ERC-20 tokens).
7 new query functions: get_asset_by_coingecko_id, get_asset_by_cmc_id,
get_assets_by_base_asset, get_assets_by_blockchain,
get_assets_by_unified_symbol.
33 new tests covering: Layer 1 identity, Layer 2 cross-system identifiers,
identifier query functions, identifier consistency (uniqueness, derivation),
exchange registry.
Total: 1189 tests, 47 files, all green.
Based on research: CCXT BASE/QUOTE is de facto standard, CoinGecko ID
most widely used in crypto-native, ISO 24165 DTI emerging, FIGI for
institutional.
2026-07-12 00:40:14 +02:00
Codex
d2d0c5e292
malkhut: multi-exchange asset universe + schema docs
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ExchangeProfile: standardized exchange metadata (fees, latency, capabilities).
3 pre-defined exchanges: Binance, BingX, Bybit.
AssetProfile.exchanges: tuple[str] — which venues trade each asset.
New query functions: get_assets_on_exchange, get_common_assets,
get_exchange_for_asset, get_exchange, list_exchanges.
_DATA_STORAGE_SCHEMA_FORMATS.md: comprehensive reference for agent
consumption — data model, storage format, query interfaces, data flow
diagram, enum reference, import patterns for BLUE/VIOLET/UV integration.
README updated: exchange registry section, package structure, subsystems
table.
2026-07-11 22:37:15 +02:00
Codex
aaaf326abf
malkhut(docs): add parallel eval subsystem to README + update date
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- Added parallel_eval.py to package structure tree
- Added Parallel Eval row to completed subsystems table
- Updated CWM row with numba timing (5.3 µs/transition)
- Date updated to 2026-07-11
2026-07-11 21:12:17 +02:00
Codex
be0e1468da
malkhut(perf): parallel episode eval — 9x single-eval speedup, zero fidelity loss
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- parallel_eval.py: ProcessPoolExecutor-based episode runner. Each worker
gets its own CWM + planner instance. Zero shared state = embarrassingly
parallel. Deterministic: same seed → same result.
- PolicyEvaluator.evaluate_candidate: new workers parameter (0=sequential,
>1=parallel). Backward compatible: default workers=0.
- 16 new tests: determinism, pickling, result validity, cross-validation
between sequential and parallel paths, backward compatibility.
- README: training performance table with speedup measurements.
Speedup results (3 assets × 30 scenarios = 90 scenarios):
Sequential: 3.3s per eval (1.0x)
2 workers: 1.3s per eval (2.6x)
4 workers: 0.6s per eval (5.9x)
8 workers: 0.4s per eval (9.1x)
CMA-ES 48 evals: 125s → 85s (1.5x training speedup)
Note: CWM numba hot path was already wired (_HAS_NUMBA=True, 5.3µs/transition).
Bottleneck is MCTS planner (96% of eval time), not CWM.
2026-07-11 20:19:32 +02:00
Codex
981b469d51
malkhut: asset classification, behavior DSL, auto-compiler, behavior-driven scenarios
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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)
2026-07-11 06:37:17 +02:00
