DOLPHIN/prod/docs/SYSTEM_FILE_MAP.md

DOLPHIN-NAUTILUS System File Map

Authoritative location reference for all critical code, engines, and data

Version: 1.0 Date: 2026-03-22 Scope: All subsystems running on DOLPHIN (Linux) with cross-platform path references where shares exist

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TABLE OF CONTENTS

1. Path Resolution — Cross-Platform Key
2. Subsystem A — Alpha Engine Core
3. Subsystem B — Nautilus Trader Integration
4. Subsystem C — Order Book Features (OBF)
5. Subsystem D — External Factors (ExF)
6. Subsystem E — Eigenvalue Scanner & ACB
7. Subsystem F — Monte Carlo Forewarner
8. Subsystem G — Prefect Orchestration
9. Subsystem H — Hazelcast In-Memory Grid
10. Subsystem I — ML Models & DVAE
11. Subsystem J — Survival Stack / Watchdog
12. Data Locations — All Stores
13. Config & Deployment Files
14. Test Suites
15. Utility & Scripts

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1. PATH RESOLUTION

Authoritative resolver: nautilus_dolphin/dolphin_paths.py

All code consuming shared data MUST import from this module — never hardcode paths.

from dolphin_paths import (
    get_arb512_storage_root,   # NG3/NG6 correlation_arb512 root
    get_eigenvalues_path,      # per-date eigenvalue + ExF snapshots
    get_project_root,          # NG5 Predict root
    get_vbt_cache_dir,         # VBT vector parquet cache
    get_klines_dir,            # 5yr 5s klines parquet
    get_arrow_backfill_dir,    # Arrow + synthetic backfill
)

Cross-Platform Path Table

Logical Name	Linux Path	Windows Path	SMB Share
NG3/NG6 Data Root	/mnt/ng6_data	C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512	DolphinNG6_Data
Eigenvalues	/mnt/ng6_data/eigenvalues/	…\correlation_arb512\eigenvalues\	DolphinNG6_Data
NG5 Predict Root	/mnt/dolphin	C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict	DolphinNG5_Predict
VBT Cache (vectors)	/mnt/dolphin/vbt_cache/	…\DOLPHIN NG HD HCM TSF Predict\vbt_cache\	DolphinNG5_Predict
Klines (5s, preferred)	/mnt/dolphin/vbt_cache_klines/	…\DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines\	DolphinNG5_Predict
Arrow Backfill	/mnt/dolphin/arrow_backfill/	…\DOLPHIN NG HD HCM TSF Predict\arrow_backfill\	DolphinNG5_Predict
DOLPHIN Local Root	/mnt/dolphinng5_predict/	(local on DOLPHIN machine)	—
NG6 Sparse	/mnt/ng6/	—	DolphinNG6

SMB Server: 100.119.158.61 (Tailscale, Windows machine) Prefect Server: http://100.105.170.6:4200 (Tailscale) Hazelcast: localhost:5701 (Docker on DOLPHIN)

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2. ALPHA ENGINE CORE

The NDAlphaEngine is the heart of the system. All alpha logic lives here — the Nautilus layer is a thin wire.

Primary Files

File	Purpose
nautilus_dolphin/nautilus_dolphin/nautilus/esf_alpha_orchestrator.py	MAIN ENGINE — NDAlphaEngine, 7-layer alpha stack, begin_day/step_bar/end_day API
nautilus_dolphin/nautilus_dolphin/nautilus/alpha_signal_generator.py	Layer 1+2: vel_div gate, DC, OB-Sub2 signal confirmation
nautilus_dolphin/nautilus_dolphin/nautilus/alpha_asset_selector.py	Layer 5: IRP asset selection, numba kernels (compute_irp_nb, rank_assets_irp_nb)
nautilus_dolphin/nautilus_dolphin/nautilus/alpha_bet_sizer.py	Layer 6: cubic-convex leverage sizing, numba compute_sizing_nb
nautilus_dolphin/nautilus_dolphin/nautilus/alpha_exit_manager.py	Layer 7: TP/SL/max_hold/OB-dynamic exit logic
nautilus_dolphin/nautilus_dolphin/nautilus/proxy_boost_engine.py	ProxyBoostEngine wrapper (ACBv6 pre-compute, pre_bar_proxy_update)
nautilus_dolphin/nautilus_dolphin/nautilus/esf_alpha_orchestrator_AGENT_fork.py	Agent-modified fork (preserve for diff/rollback)

Sub-Components Called by NDAlphaEngine

File	Role
nautilus_dolphin/nautilus_dolphin/nautilus/adaptive_circuit_breaker.py	ACBv6 — 3-scale regime size multiplier; injected via engine.set_acb()
nautilus_dolphin/nautilus_dolphin/nautilus/ob_features.py	OBFeatureEngine — aggregates OB signals; injected via engine.set_ob_engine()
nautilus_dolphin/nautilus_dolphin/nautilus/volatility_detector.py	VolatilityRegimeDetector — 50-bar BTC return std gate
nautilus_dolphin/nautilus_dolphin/nautilus/position_manager.py	PositionManager — tracks open positions
nautilus_dolphin/nautilus_dolphin/nautilus/circuit_breaker.py	CircuitBreakerManager — per-asset trip logic
nautilus_dolphin/nautilus_dolphin/nautilus/metrics_monitor.py	MetricsMonitor — runtime performance metrics

Champion State

• Config: prod/configs/blue.yml
• Performance log: nautilus_dolphin/run_logs/summary_20260307_163401.json
• Canonical backtest results: nautilus_dolphin/backtest_results/ (47+ JSON files)
• VBT gold result: nautilus_dolphin/backtest_results/dolphin_vbt_real_champion.json (if present)

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3. NAUTILUS TRADER INTEGRATION

Nautilus Trader (v1.219.0, siloqy-env) provides the HFT execution kernel.

Core Integration Files

File	Purpose
nautilus_dolphin/nautilus_dolphin/nautilus/dolphin_actor.py	DolphinActor(Strategy) — Nautilus wrapper; on_start/on_bar/on_stop lifecycle; ACB pending-flag pattern; HZ integration
nautilus_dolphin/nautilus_dolphin/nautilus/strategy.py	DolphinExecutionStrategy — signal-level strategy; VolatilityRegimeDetector filter
nautilus_dolphin/nautilus_dolphin/nautilus/strategy_config.py	DolphinStrategyConfig(StrategyConfig, frozen=True) — typed champion params
nautilus_dolphin/nautilus_dolphin/nautilus/launcher.py	NautilusDolphinLauncher — TradingNode setup (live trading, future use)
nautilus_dolphin/nautilus_dolphin/nautilus/backtest_engine.py	Nautilus BacktestEngine wrapper helpers
nautilus_dolphin/nautilus_dolphin/nautilus/backtest_runner.py	Backtest orchestration
nautilus_dolphin/nautilus_dolphin/nautilus/data_adapter.py	Data pipeline adapter (generic)
nautilus_dolphin/nautilus_dolphin/nautilus/arrow_data_adapter.py	Arrow format data adapter
nautilus_dolphin/nautilus_dolphin/nautilus/parquet_data_adapter.py	Parquet format data adapter
nautilus_dolphin/nautilus_dolphin/nautilus/arrow_parquet_catalog_builder.py	Nautilus data catalog builder
nautilus_dolphin/nautilus_dolphin/nautilus/execution_client.py	Order execution client
nautilus_dolphin/nautilus_dolphin/nautilus/trade_logger.py	TradeLoggerActor — independent CSV/JSON session logger.
nautilus_dolphin/nautilus_dolphin/nautilus/smart_exec_algorithm.py	SmartExecAlgorithm — execution algo
nautilus_dolphin/nautilus_dolphin/nautilus/signal_bridge.py	SignalBridgeActor — signal distribution

Runners / Entry Points

File	Purpose
prod/run_nautilus.py	Standalone CLI: BacktestEngine + DolphinActor, single day
prod/paper_trade_flow.py	Primary daily flow — NDAlphaEngine direct (00:05 UTC)
prod/nautilus_prefect_flow.py	Nautilus supervisor flow — BacktestEngine + DolphinActor (00:10 UTC); champion hash check; HZ heartbeats
prod/ops/launch_paper_portfolio.py	Phoenix-01 Paper Launcher — launches high-fidelity paper portfolio; realistic friction.

Nautilus Config

File	Purpose
nautilus_dolphin/config/config.yaml	Nautilus runtime config — signals, strategy params, exchange, execution, Redis
prod/configs/blue.yml	Champion SHORT config (FROZEN) — all 15 champion params
prod/configs/green.yml	Bidirectional config (staging — pending LONG validation)

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4. ORDER BOOK FEATURES

OBF subsystem ingests Binance WS order books at ~500ms, computes 4 sub-system features, and pushes to Hazelcast.

Source Files

File	Purpose
prod/obf_prefect_flow.py	OBF hot loop — WS ingestion, feature computation, HZ push, file cache write
prod/obf_persistence.py	OBFPersistenceService — Parquet archival (asset=/date=/ partition)
nautilus_dolphin/nautilus_dolphin/nautilus/ob_provider.py	OBProvider base class + OBSnapshot dataclass
nautilus_dolphin/nautilus_dolphin/nautilus/hz_ob_provider.py	HZOBProvider — reads OB features from HZ shards; dynamic asset discovery
nautilus_dolphin/nautilus_dolphin/nautilus/hz_sharded_feature_store.py	Sharded HZ feature store (SHARD_COUNT=10, routing by symbol hash)
nautilus_dolphin/nautilus_dolphin/nautilus/ob_placer.py	OBPlacer — SmartPlacer fill probability computation
scripts/verify_parquet_archive.py	Parquet archive integrity checker (schema, gaps, corrupt files)

OBF Data Locations

Store	Path / Key	Notes
Live OB features	HZ DOLPHIN_FEATURES_SHARD_00..09	Per-asset, pushed every ~500ms; shard = sum(ord(c)) % 10
File fallback cache	/mnt/dolphinng5_predict/ob_cache/latest_ob_features.json	Atomic tmp→rename; staleness threshold 5s
Parquet archive	/mnt/ng6_data/ob_features/asset=*/date=*/*.parquet	Long-term history; ~500ms resolution
OB raw data	/mnt/dolphinng5_predict/ob_data/	Raw WS snapshots

OBF Schema Reference

• ob_cache/SCHEMA.md — full field reference for latest_ob_features.json
• prod/OBF_SUBSYSTEM.md → now at prod/docs/OBF_SUBSYSTEM.md

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5. EXTERNAL FACTORS (ExF)

ExF subsystem fetches macro/sentiment data (funding, dvol, fear&greed, taker ratio) and pushes to HZ for ACBv6 consumption.

Source Files

File	Purpose
prod/exf_fetcher_simple.py	Primary ExF daemon — live fetcher v2.1, pushes to HZ
prod/exf_fetcher_flow.py	Prefect ExF flow (main)
prod/exf_fetcher_flow_fast.py	Prefect ExF flow (fast variant)
prod/exf_prefect_production.py	ExF Prefect production runner
prod/exf_persistence.py	ExF persistence service
prod/exf_integrity_monitor.py	ExF data integrity monitoring
prod/realtime_exf_service.py	Real-time ExF service daemon
prod/serve_exf.py	ExF HTTP API server
prod/deploy_exf.py / deploy_exf_v3.py	Deployment scripts

ExF Data Locations

Store	Path / Key	Notes
Live ExF	HZ DOLPHIN_FEATURES["acb_boost"]	JSON {boost, beta} — consumed by DolphinActor listener
ExF snapshots	/mnt/ng6_data/eigenvalues/YYYY-MM-DD/extf_snapshot_*__Indicators.npz	Per-scan NPZ, loaded by ACBv6
ESOF snapshots	/mnt/ng6_data/eigenvalues/YYYY-MM-DD/esof_snapshot_*__Indicators.npz	Order flow NPZ
Local ExF cache	/mnt/dolphinng5_predict/external_factors/eso_cache/	ESO (equity short option) cache

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6. EIGENVALUE SCANNER & ACB

The NG3 correlation scanner runs on the Windows machine, producing per-scan eigenvalue data that DOLPHIN reads over SMB.

Source Files (DOLPHIN side)

File	Purpose
nautilus_dolphin/nautilus_dolphin/nautilus/adaptive_circuit_breaker.py	ACBv6 — reads ExF NPZ, computes 3-scale regime_size_mult
prod/acb_processor_service.py	ACB processor service — daily ACB boost computation + HZ write (uses HZ CP Subsystem lock)
prod/esof_prefect_flow.py	ESOF feature persistence flow
prod/esof_persistence.py	ESOF persistence service
prod/esof_update_flow.py	ESOF incremental update

Eigenvalue Data Locations

Store	Linux Path	Windows Path	Content
Daily eigenvalue scans	/mnt/ng6_data/eigenvalues/YYYY-MM-DD/	…\correlation_arb512\eigenvalues\YYYY-MM-DD\	scan_.npz, scan___Indicators.npz
512-bit correlation matrices	/mnt/ng6_data/matrices/YYYY-MM-DD/ (ACL restricted)	…\correlation_arb512\matrices\YYYY-MM-DD\	scan_w50.arb512.pkl.zst
VBT klines (5s, primary)	/mnt/dolphin/vbt_cache_klines/YYYY-MM-DD.parquet	…\vbt_cache_klines\YYYY-MM-DD.parquet	1439 rows × 57 cols: vel_div, v50/v150/v300/v750, instability, 48 asset prices
Local klines	/mnt/dolphinng5_predict/vbt_cache_klines/YYYY-MM-DD.parquet	—	Same schema; DOLPHIN-local copy used by prod flows
Arrow backfill	/mnt/dolphin/arrow_backfill/YYYY-MM-DD/	…\arrow_backfill\YYYY-MM-DD\	Arrow format, ~5yr history + synthetic
VBT vectors	/mnt/dolphin/vbt_cache/*.parquet	…\vbt_cache\*.parquet	~1.7K files, vector format

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7. MONTE CARLO FOREWARNER

ML-based regime forewarning system that modulates ACBv6 scale (mc_scale).

Source Files

File	Purpose
prod/mc_forewarner_flow.py	MC Forewarner Prefect flow — prediction + HZ push
nautilus_dolphin/mc_forewarning_qlabs_fork/mc/mc_ml.py	DolphinForewarner ML model (loaded via engine.set_mc_forewarner())
prod/run_gold_monte_carlo.py	Gold MC runner
nautilus_dolphin/dvae/exp[1-15]_*.py	Systematic DVAE experiments (sizing, exit, coupling, leverage, liquidation)

MC Data & Model Locations

Store	Path	Notes
MC models (prod)	prod/mc_results/models/	Production-frozen model files
MC models (alt)	nautilus_dolphin/mc_results/models/	Subproject-local models
MC manifests	prod/mc_results/manifests/	Batch run manifests
MC results	prod/mc_results/results/	Output PnL/metrics JSON
MC SQLite index	prod/mc_results/mc_index.sqlite	Result index DB
QLabs fork	nautilus_dolphin/mc_forewarning_qlabs_fork/	Forewarner fork with benchmark results
ML runs (MLflow)	/mnt/dolphinng5_predict/mlruns/	MLflow experiment tracking

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8. PREFECT ORCHESTRATION

Prefect 3.6.22 (siloqy-env) orchestrates all daily flows.

Flow Files

File	Schedule	Purpose
prod/paper_trade_flow.py	00:05 UTC daily	Primary paper trade — NDAlphaEngine direct; loads klines; pushes PnL + state to HZ
prod/nautilus_prefect_flow.py	00:10 UTC daily	Nautilus BacktestEngine supervisor; champion hash check; HZ heartbeat
prod/obf_prefect_flow.py	Continuous (~500ms)	OBF hot loop — WS → compute → HZ push + JSON cache
prod/exf_fetcher_flow.py	Periodic	ExF data fetch + persistence
prod/mc_forewarner_flow.py	Daily	MC regime prediction + HZ write
prod/vbt_backtest_flow.py	On-demand	VBT backtest orchestration
prod/vbt_cache_update_flow.py	Periodic	Incremental klines cache update
prod/esof_prefect_flow.py	Periodic	Order flow feature persistence

Deployment Configs

File	Deployment Name	Work Pool
prod/exf_deployment.yaml	ExF fetcher	dolphin
prod/obf_deployment.yaml	OBF hot loop	dolphin
prod/esof_deployment.yaml	ESOF features	dolphin

Prefect Infrastructure

Component	Location
Prefect Server	Docker container, port 4200 (Tailscale: 100.105.170.6:4200)
Prefect Worker	prefect worker start --pool dolphin --type process (siloqy-env)
API URL	http://localhost:4200/api (on DOLPHIN)
Docker Compose	prod/docker-compose.yml

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9. HAZELCAST IN-MEMORY GRID

Hazelcast acts as the system memory — real-time shared state between all subsystems.

Infrastructure

Component	Detail
Version	Hazelcast 5.3 (Docker)
Address	localhost:5701
Cluster name	dolphin
CP Subsystem	Enabled — used for atomic operations (ACB processor)
Management Center	localhost:8080
Client (Python)	hazelcast-python-client 5.6.0 (siloqy-env)
Docker Compose	prod/docker-compose.yml

IMap Schema — All Named Maps

IMap Name	Key	Value	Writer	Reader(s)
DOLPHIN_SAFETY	"latest"	JSON {posture, Rm, sensors, ...}	system_watchdog_service.py	DolphinActor, paper_trade_flow, nautilus_prefect_flow
DOLPHIN_FEATURES	"acb_boost"	JSON {boost, beta}	acb_processor_service.py	DolphinActor (HZ listener)
DOLPHIN_FEATURES	"latest_eigen_scan"	JSON {vel_div, scan_number, asset_prices, timestamp_ns, ...}	Eigenvalue scanner bridge	DolphinActor (live mode)
DOLPHIN_PNL_BLUE	"YYYY-MM-DD"	JSON daily result dict	paper_trade_flow, DolphinActor._write_result_to_hz	Analytics, monitoring
DOLPHIN_PNL_GREEN	"YYYY-MM-DD"	JSON daily result dict	paper_trade_flow (green)	Analytics
DOLPHIN_STATE_BLUE	"latest"	JSON {strategy, capital, date, peak_capital, drawdown, engine_state, ...}	paper_trade_flow	paper_trade_flow (capital restore)
DOLPHIN_STATE_BLUE	"latest_nautilus"	JSON {capital, param_hash, posture, engine, ...}	nautilus_prefect_flow	nautilus_prefect_flow (capital restore)
DOLPHIN_STATE_BLUE	"state_{strategy}_{date}"	JSON per-run snapshot	paper_trade_flow	Recovery
DOLPHIN_HEARTBEAT	"nautilus_flow_heartbeat"	JSON {ts, iso, phase, run_date, ...}	nautilus_prefect_flow	External monitoring
DOLPHIN_HEARTBEAT	"probe_ts"	Timestamp string	nautilus_prefect_flow (hz_probe_task)	Liveness
DOLPHIN_META_HEALTH	"latest"	JSON report	meta_health_daemon.py	Monitoring / Dashboards
DOLPHIN_OB	per-asset key	JSON OB snapshot	obf_prefect_flow	HZOBProvider
DOLPHIN_FEATURES_SHARD_00	symbol	JSON OB feature dict	obf_prefect_flow	HZOBProvider
DOLPHIN_FEATURES_SHARD_01..09	symbol	JSON OB feature dict	obf_prefect_flow	HZOBProvider
DOLPHIN_SIGNALS	signal key	Signal distribution	signal_bridge.py	Strategy consumers

HZ Shard Routing (OB Features)

SHARD_COUNT = 10
shard_idx = sum(ord(c) for c in symbol) % SHARD_COUNT
imap_name = f"DOLPHIN_FEATURES_SHARD_{shard_idx:02d}"

Routing is deterministic and requires no config. 400+ assets distribute evenly.

HZ CP Subsystem

Used by acb_processor_service.py for distributed locking. CP Subsystem must be enabled in Hazelcast config (see docker-compose.yml).

Key Source Files

File	HZ Role
prod/acb_processor_service.py	Acquires CP lock; writes DOLPHIN_FEATURES["acb_boost"] daily
prod/_hz_push.py	Generic HZ push utility (ad-hoc writes)
prod/system_watchdog_service.py	Writes DOLPHIN_SAFETY (posture + Rm)
prod/obf_prefect_flow.py	Fire-and-forget per-asset writes to OB shards; circuit breaker on 5+ failures
nautilus_dolphin/nautilus_dolphin/nautilus/hz_ob_provider.py	Reads OB shards; lazy connect; dynamic asset discovery from key_set
nautilus_dolphin/nautilus_dolphin/nautilus/hz_sharded_feature_store.py	Shard routing + bulk read/write
nautilus_dolphin/nautilus_dolphin/nautilus/scan_hz_bridge.py	Scan data → HZ bridge
nautilus_dolphin/nautilus_dolphin/nautilus/dolphin_actor.py	add_entry_listener on DOLPHIN_FEATURES["acb_boost"]; pending-flag pattern

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10. ML MODELS & DVAE

Model Files

File / Directory	Purpose
/mnt/dolphinng5_predict/models/hcm_model.json	HCM neural network weights (12 MB)
/mnt/dolphinng5_predict/models/tsf_model.json	TSF (time-series forecaster) weights
/mnt/dolphinng5_predict/models/tsf_engine.pkl	TSF engine pickle
/mnt/dolphinng5_predict/models/convnext_dvae_ML/	ConvNeXt D-VAE ML directory
/mnt/dolphinng5_predict/trained_models/	Additional model checkpoints
nautilus_dolphin/mc_forewarning_qlabs_fork/	MC Forewarner model + benchmarks
prod/mc_results/models/	Production-frozen MC models

DVAE Experiments

Location	Content
nautilus_dolphin/dvae/	47+ experiment scripts
nautilus_dolphin/dvae/exp[1-15]_*.py	Systematic: sizing, exit coupling, leverage guards, liquidation, proxy
nautilus_dolphin/dvae/convnext_dvae.py	ConvNeXt model
nautilus_dolphin/dvae/flint_dvae_kernel.py	Flint-precision VAE kernel (512-bit path)
nautilus_dolphin/dvae/exp_shared.py	Shared experiment utilities

Training Infrastructure

Location	Content
/mnt/dolphin_training/	Training data and scripts
/mnt/dolphinng5_predict/training_reports/	Training logs and metrics
/mnt/dolphinng5_predict/checkpoints/	Strategy checkpoints
/mnt/dolphinng5_predict/checkpoints_10k/	10K-step checkpoints
/mnt/dolphinng5_predict/checkpoints_production/	Production-frozen checkpoints
/mnt/dolphinng5_predict/mlruns/	MLflow experiment tracking

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11. SURVIVAL STACK / WATCHDOG

Source Files

File	Purpose
prod/system_watchdog_service.py	5-sensor Rm computation → writes DOLPHIN_SAFETY to HZ
prod/meta_health_daemon.py	Meta-System Monitoring (MHD) — Watchdog-of-Watchdogs
nautilus_dolphin/nautilus_dolphin/nautilus/survival_stack.py	SurvivalStack class — sensor aggregation logic
nautilus_dolphin/nautilus_dolphin/nautilus/macro_posture_switcher.py	Macro regime posture switching

MHD (Meta) Specifics

File	Role
prod/meta_health_daemon.service	Systemd unit (Linux)
prod/meta_health_daemon_bsd.rc	rc.d script (FreeBSD)
run_logs/meta_health.json	Latest MHD report
run_logs/meta_health.log	MHD persistent log

Posture Table

Posture	Rm threshold	Effect
APEX	Rm ≥ 0.90	Full operation
STALKER	Rm ≥ 0.75	max_leverage capped to 2.0
TURTLE	Rm ≥ 0.50	abs_max_leverage × Rm
HIBERNATE	Rm < 0.50	on_bar() returns immediately; no trading

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12. DATA LOCATIONS

12.1 Live / Real-Time Data (Hazelcast)

See §9 IMap Schema above — Hazelcast is the primary real-time data bus.

All live feature data (OB, ExF, ACB, scan, posture) flows through Hazelcast. Consumers must treat HZ as a data source, not a cache.

12.2 Scan Data (Eigenvalues)

Dataset	Linux	Windows
Daily scan NPZ	/mnt/ng6_data/eigenvalues/YYYY-MM-DD/	C:\Users\Lenovo\Documents\- Dolphin NG HD (NG3)\correlation_arb512\eigenvalues\YYYY-MM-DD\
512-bit matrices	/mnt/ng6_data/matrices/YYYY-MM-DD/ (ACL restricted)	…\correlation_arb512\matrices\YYYY-MM-DD\

12.3 Klines / VBT Cache (Primary Replay Source)

Dataset	Linux	Windows
5s klines (preferred)	/mnt/dolphin/vbt_cache_klines/YYYY-MM-DD.parquet	…\DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines\YYYY-MM-DD.parquet
VBT vector cache	/mnt/dolphin/vbt_cache/*.parquet	…\vbt_cache\*.parquet
Arrow backfill	/mnt/dolphin/arrow_backfill/YYYY-MM-DD/	…\arrow_backfill\YYYY-MM-DD\
Local klines copy	/mnt/dolphinng5_predict/vbt_cache_klines/YYYY-MM-DD.parquet	(DOLPHIN-local)

Klines schema: 1439 rows × 57 cols per day. Columns: vel_div, v50/v150/v300/v750_lambda_max_velocity, instability_50/150, + 48 asset close prices.

12.4 OB Features

Dataset	Path	Notes
Live OB (HZ)	DOLPHIN_FEATURES_SHARD_00..09	~500ms latency
OB JSON fallback	/mnt/dolphinng5_predict/ob_cache/latest_ob_features.json	5s staleness threshold
OB archive (Parquet)	/mnt/ng6_data/ob_features/asset=*/date=*/*.parquet	Partitioned; see SCHEMA.md
OB raw data	/mnt/dolphinng5_predict/ob_data/	Raw WS snapshots

12.5 Paper Trade Logs & Results

Dataset	Path
Blue paper logs	prod/paper_logs/blue/paper_pnl_YYYY-MM.jsonl
Green paper logs	prod/paper_logs/green/paper_pnl_YYYY-MM.jsonl
E2E trade CSVs	prod/paper_logs/blue/E2E_trades_YYYY-MM-DD.csv
E2E bar CSVs	prod/paper_logs/blue/E2E_bars_YYYY-MM-DD.csv
Session Settings	logs/paper_trading/settings_*.json
Trade Audit	logs/paper_trading/trades_*.csv
Nautilus run summary	HZ DOLPHIN_STATE_BLUE["latest_nautilus"]
Run logs JSON	nautilus_dolphin/run_logs/summary_*.json

12.6 Backtest Results & MC

Dataset	Path
Champion backtest	nautilus_dolphin/backtest_results/ (47+ JSON)
MC results (prod)	prod/mc_results/results/
MC models	prod/mc_results/models/
MC SQLite index	prod/mc_results/mc_index.sqlite
2-week backtest	/mnt/dolphinng5_predict/backtest_results_2week/
Paper 1-week	/mnt/dolphinng5_predict/paper_trading_1week_results/
Paper 1-month	/mnt/dolphinng5_predict/paper_trading_1month_results/
Rolling 10-week	/mnt/dolphinng5_predict/rolling_10week_results/

12.7 Models

Model	Path
HCM neural net	/mnt/dolphinng5_predict/models/hcm_model.json
TSF forecaster	/mnt/dolphinng5_predict/models/tsf_model.json
TSF engine	/mnt/dolphinng5_predict/models/tsf_engine.pkl
DVAE ConvNeXt	/mnt/dolphinng5_predict/models/convnext_dvae_ML/

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13. CONFIG & DEPLOYMENT FILES

File	Purpose
prod/docker-compose.yml	Docker: Hazelcast 5.3 (port 5701), Management Center (8080), Prefect Server (4200)
prod/configs/blue.yml	Champion SHORT — FROZEN
prod/configs/green.yml	Bidirectional staging
prod/obf_deployment.yaml	OBF Prefect deployment (Prefect work pool: dolphin)
prod/exf_deployment.yaml	ExF Prefect deployment
prod/esof_deployment.yaml	ESOF Prefect deployment
nautilus_dolphin/config/config.yaml	Nautilus runtime config (signals, strategy, exchange, Redis localhost:6379)
nautilus_dolphin/pyproject.toml	Package config, pytest settings

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14. TEST SUITES

Nautilus / Alpha Engine Tests (nautilus_dolphin/tests/)

File	Tests	Coverage
test_0_nautilus_bootstrap.py	11	Import chain, NautilusKernelConfig, ACB, CircuitBreaker, launcher
test_dolphin_actor.py	35	DolphinActor lifecycle, ACB thread-safety, HIBERNATE, date change, HZ degradation
test_adaptive_circuit_breaker.py	~10	ACBv6 3-scale computation, cut-to-size
test_circuit_breaker.py	~6	CircuitBreakerManager
test_volatility_detector.py	~6	VolatilityRegimeDetector
test_strategy.py	~5	DolphinExecutionStrategy signal filters
test_position_manager.py	~5	PositionManager
test_smart_exec_algorithm.py	~6	SmartExecAlgorithm
test_signal_bridge.py	~4	SignalBridgeActor
test_metrics_monitor.py	~4	MetricsMonitor
test_acb_standalone.py	~8	ACB standalone (no Nautilus)
test_acb_nautilus_vs_reference.py	~6	ACB parity: Nautilus vs reference impl
test_nd_vs_standalone_comparison.py	~8	NDAlphaEngine vs standalone VBT parity
test_trade_by_trade_validation.py	~10	Trade-by-trade result validation
test_proxy_boost_production.py	~8	ProxyBoostEngine production checks
test_strategy_registration.py	~4	Strategy registration
test_redis_integration.py	~4	Redis signal integration

OBF Tests (tests/)

File	Tests	Coverage
tests/test_obf_unit.py	~120	OBF subsystem: stream service, HZOBProvider, stale detection, crossed-book, buffer replay

CI Tests (ci/)

File	Coverage
ci/test_algo3_datasource_parity.py	Algo3 data source parity
ci/test_01_data_pipeline.py	Data pipeline

Run Command

source /home/dolphin/siloqy_env/bin/activate
cd /mnt/dolphinng5_predict
python -m pytest nautilus_dolphin/tests/ -v        # Nautilus/alpha suite
python -m pytest tests/test_obf_unit.py -v         # OBF suite

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15. UTILITY & SCRIPTS

File	Purpose
nautilus_dolphin/dolphin_paths.py	CRITICAL — cross-platform path resolver
scripts/verify_parquet_archive.py	OBF Parquet archive integrity checker
prod/_hz_push.py	Ad-hoc HZ push utility
prod/klines_backfill_5y_10y.py	Historical klines builder (5yr/10yr)
prod/continuous_convert.py	Continuous Arrow→Parquet converter
prod/convert_arrow_to_parquet_batch.py	Batch Arrow→Parquet conversion
prod/certify_extf_gold.py	ExF gold certification
prod/diag_5day.py	5-day system diagnostics
prod/extract_spec.py	Specification extractor

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16. BACKUP & FROZEN STATES

Path	Content
/mnt/dolphinng5_predict/FROZEN_BACKUP_20260208/	System freeze Feb 8 2026 (alpha_engine + exit_matrix_engine)
/mnt/dolphinng5_predict/alpha_engine_BACKUP_20260202_143018/	Alpha engine pre-refactor backup
/mnt/dolphinng5_predict/alpha_engine_BACKUP_20260209_203911/	Alpha engine post-refactor backup
/mnt/dolphinng5_predict/alpha_engine_BASELINE_75PCT_EDGE/	Baseline 75% edge reference
/mnt/dolphinng5_predict/backups_20260104/	Jan 4 2026 backup

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17. DOCUMENTATION INDEX (prod/docs/)

File	Content
SYSTEM_BIBLE.md	THE BIBLE — full doctrinal reference, current (v2 MIG7+Nautilus+Prefect)
SYSTEM_BIBLE_v1_MIG7_20260307.md	Bible fork — system state as of 2026-03-07, MIG7 complete
NAUTILUS_DOLPHIN_SPEC.md	Nautilus-DOLPHIN implementation spec (v1.0, 2026-03-22)
SYSTEM_FILE_MAP.md	THIS FILE — authoritative file/data location reference
PRODUCTION_BRINGUP_MASTER_PLAN.md	Production bringup checklist
BRINGUP_GUIDE.md	System bringup guide
OBF_SUBSYSTEM.md	OBF architecture reference (Sprint 1)
NAUTILUS_INTEGRATION_ROADMAP.md	Nautilus integration roadmap
E2E_MASTER_PLAN.md	End-to-end master plan
EXTF_PROD_BRINGUP.md	ExF production bringup
EXTF_SYSTEM_PRODUCTIZATION_DETAILED_LOG.md	ExF productization log
EXF_V2_DEPLOYMENT_SUMMARY.md	ExF v2 deployment summary
LATENCY_OPTIONS.md	Latency analysis options
KLINES_5Y_10Y_DATASET_README.md	Klines dataset readme
AGENT_CHANGE_ANALYSIS_REPORT.md	Agent change analysis
AGENT_READ_CRITICAL__CHANGES_TO_ENGINE.md	Critical engine changes
NAUTILUS-DOLPHIN Prod System Spec_...FLAWS.md	ChatGPT Survival Stack design flaws analysis

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File Map version 1.0 — 2026-03-22 — DOLPHIN-NAUTILUS System