Database connectivity defaults to a 5 second PostgreSQL connect timeout. Override with DATABASE_CONNECT_TIMEOUT when needed. Set MIGRATIONS_OUTPUT_JSON=1 when running run_migrations.py for a machine-readable migration summary.

Hard mainline requirement

All trading-analysis development must strictly follow docs/ICT交易全书_出版终校版.md and docs/AI_ICT_硬性主线约束_必须遵循ICT交易全书_2026-05-21_v1.md.

The trader-facing product must stay on this chain:

Range / Draw / Location
-> Liquidity / Sweep / Inducement / Path
-> Displacement / MSS / FVG / Retest / Session
-> backtest evidence
-> pre-market plan
-> intraday unique focus or no-trade reason
-> journal / study-notes feedback

Any capability that cannot serve this chain must stay out of the trader-facing UI and, if still needed for operations, belongs under System Ops.

Current closed loop

当前主线可运行链路：

OKX -> candles -> market_sessions -> swings -> dealing_ranges -> liquidity_pools -> sweep_events -> displacement_events -> MSS -> FVG -> bias_snapshots -> trade_signals -> trade_executions -> backtest_runs -> backtest_results -> reports

Training Case Engine milestone index

Training Case Engine is the external-facing product training module. The public milestone state is M1-M40, with current product maturity through M40.

Accepted milestone range: M1-M40, with current product maturity through M40.

Offline validation commands:

.venv/bin/python -m unittest discover -s tests/unit -p "test_*.py"
.venv/bin/python scripts/run_offline_checks.py

Accepted user-facing surfaces:

Trading Workbench at /
Training Console at /training
public API routes
offline public package import/export
public error catalog
public privacy contract coverage
Acceptance and offline validation: M35 acceptance posture, Training Case acceptance manifest, focused milestone test suites, offline checks, and public validation.
Public API and privacy contracts: API contract coverage, error contract coverage, and privacy contract coverage keep public routes audited while public responses scrub private solution material and internal solution details.
Package smoke and offline registry: offline package smoke covers local import/export with public package, public export, and public output flows; broader offline checks register training_case_package.
Dashboard and public docs privacy: trading workbench syntax validation is registered as dashboard_js_syntax, training console syntax validation is registered as training_console_js_syntax, and public docs privacy smoke coverage keeps Training content to public case fields only.
Local Docker guard: single-set local Docker deployment uses the documented rebuild order and Docker smoke guard coverage for one active deployment set, one ai-ict container, and healthy status.

Training public error catalog

Training API validation failures are deterministic public validation errors: 400 with invalid_json_body, 400 with missing_required_json_field:<field>, 400 with invalid_json_field:<field>, 400 with invalid_path_param:<field>, or 400 with invalid_limit_query_param:limit.
Missing Training cases return 404 with not_found.
Masked unexpected Training API failures return 500 with internal_server_error; the public response masks raw internals, and responses do not echo raw internals.
Package CLI public errors for invalid input paths and invalid output targets emit one-line JSON with passed, input, output, and error; stable public error codes include input_file_not_found, input_path_is_directory, output_path_is_directory, and output_path_unwritable.

Public API response examples:

Malformed or non-object JSON body: POST /training/attempts -> HTTP 400

{"error": "invalid_json_body"}

Missing required JSON field: POST /training/attempts with {} -> HTTP 400

{"error": "missing_required_json_field:case_id"}

Invalid JSON field value: POST /training/attempts with text case_id -> HTTP 400

{"error": "invalid_json_field:case_id"}

Invalid path identifier: GET /training/cases/not-an-id -> HTTP 400

{"error": "invalid_path_param:case_id"}

Invalid list limit: GET /training/cases?limit=not-a-number -> HTTP 400

{"error": "invalid_limit_query_param:limit"}

Missing case: GET /training/cases/404 -> HTTP 404

{"error": "not_found"}

Masked internal server error: any Training route -> HTTP 500

{"error": "internal_server_error"}

Package CLI public error examples:

Missing input file:

{"passed": false, "input": "missing_training_case_package.json", "output": "training_case_public_export.json", "error": "input_file_not_found"}

Directory input path:

{"passed": false, "input": "training_case_package_dir", "output": "training_case_public_export.json", "error": "input_path_is_directory"}

Directory output target:

{"passed": false, "input": "tests/fixtures/training_case_package_m6.json", "output": "training_case_public_export_dir", "error": "output_path_is_directory"}

Local Docker deployment

Use the single-set local Docker deployment policy for local server deployment:

Deploy from the deploy directory /root/deploy/ai-ict.
Keep the compose project ai-ict, container ai-ict, and exactly one active deployment set.
Keep one active ai-ict container; do not create a second local stack or a second ai-ict container.
Before every local rebuild, stop stale services first, then start the same set:

cd /root/deploy/ai-ict
docker compose -p ai-ict -f /root/deploy/ai-ict/docker-compose.yml down --remove-orphans
docker compose -p ai-ict -f /root/deploy/ai-ict/docker-compose.yml up -d --build

After the rebuild, verify the same one active set, one active ai-ict container, and Docker healthy status:

docker compose -p ai-ict -f /root/deploy/ai-ict/docker-compose.yml ps
test "$(docker ps --filter label=com.docker.compose.project=ai-ict --format '{{.Names}}' | wc -l | tr -d ' ')" = "1"
test "$(docker ps --filter name=^/ai-ict$ --format '{{.Names}}' | wc -l | tr -d ' ')" = "1"
test "$(docker inspect -f '{{.State.Health.Status}}' ai-ict)" = "healthy"

Check the documented local demo readiness without starting containers; this offline check does not invoke Docker:

.venv/bin/python scripts/check_local_demo_readiness.py

Trading Delivery Checklist

Use this locked trading delivery checklist before every market validation or deploy, so the trading module can be promoted without relying on chat memory.

must-run before every market validation or deploy:

.venv/bin/python -m unittest discover -s tests/unit -p "test_*.py"
.venv/bin/python scripts/run_offline_checks.py
.venv/bin/python scripts/check_local_demo_readiness.py
.venv/bin/python scripts/run_secret_static_checks.py
.venv/bin/python scripts/check_trading_delivery_readiness.py

Database gate:

READINESS_OUTPUT_JSON=1 .venv/bin/python scripts/run_database_readiness.py
HISTORICAL_REPAIR_OUTPUT_JSON=1 .venv/bin/python scripts/run_historical_compatibility_repair.py
.venv/bin/python scripts/run_historical_compatibility_repair.py --apply-safe-repairs
HISTORICAL_REPAIR_OUTPUT_JSON=1 .venv/bin/python scripts/run_historical_compatibility_repair.py
PIPELINE_OUTPUT_JSON=1 BACKTEST_REQUIRE_QUALITY_GATE=1 .venv/bin/python scripts/run_pipeline.py

Use --apply-safe-repairs only when the dry-run output shows safe candidates. After the second dry-run, manual review is still required for needs_sweep_reference_migration and keep_version_history, and backtest_quality_gate.passed must be true before promotion.

Deploy and health check:

docker compose -p ai-ict -f /root/deploy/ai-ict/docker-compose.yml down --remove-orphans
docker compose -p ai-ict -f /root/deploy/ai-ict/docker-compose.yml up -d --build
docker compose -p ai-ict -f /root/deploy/ai-ict/docker-compose.yml ps
test "$(docker ps --filter label=com.docker.compose.project=ai-ict --format '{{.Names}}' | wc -l | tr -d ' ')" = "1"
test "$(docker ps --filter name=^/ai-ict$ --format '{{.Names}}' | wc -l | tr -d ' ')" = "1"
test "$(docker inspect -f '{{.State.Health.Status}}' ai-ict)" = "healthy"
curl -fsS http://127.0.0.1:8000/health
curl -fsS "http://127.0.0.1:8000/trading/candidates?instrument_id=1&limit=1"

Post-deploy observe:

READINESS_OUTPUT_JSON=1 .venv/bin/python scripts/run_database_readiness.py
REPORTS_OUTPUT_JSON=1 .venv/bin/python scripts/run_reports.py

The paper execution boundary remains in force here: run_pipeline.py and the execution path are still paper-only, and any API 5xx, Docker unhealthy state, or non-empty readiness reasons should stop promotion immediately.

For one-command rehearsal orchestration, use scripts/run_trading_delivery_rehearsal.py; it runs checklist drift validation, historical repair gating, readiness plus auto-backfill, guarded pipeline, optional deploy/health-check, and post-deploy observe in one scripted flow.

Rollback trigger:

git rev-parse HEAD
docker compose -p ai-ict -f /root/deploy/ai-ict/docker-compose.yml logs --tail=100 ai-ict
git worktree add /tmp/ai-ict-rollback <last-known-good-sha>
cd /tmp/ai-ict-rollback && AI_ICT_DEPLOY_PASSWORD=<set-in-shell> AI_ICT_DEPLOY_USE_SUDO=1 .venv/bin/python scripts/deploy_with_password.py --host 192.168.3.90 --user yuqei --port 22 --remote-path /home/yuqei/project/ai-exchange --remote-archive /home/yuqei/ai-ict-deploy.tgz
rm -rf /tmp/ai-ict-rollback

Record the current commit as the last-known-good SHA before deploy. If any gate above fails, capture logs first, then redeploy from the clean rollback worktree rather than mutating the active development checkout.

Runtime utility scripts

python3 scripts/run_dev.py - start the local development API server and minimal dashboard
python3 scripts/show_models.py - list exported ORM model names
python3 scripts/check_db.py - run a simple database connectivity check
python3 scripts/run_migrations.py - apply SQL files in migrations/ order
python3 scripts/run_okx_backfill.py - fetch and write sample OKX candles
python3 scripts/run_session_generator.py - generate market sessions
python3 scripts/run_swings.py - rebuild swings
python3 scripts/run_ranges.py - rebuild dealing ranges
python3 scripts/run_liquidity.py - rebuild liquidity pools
python3 scripts/run_sweeps.py - rebuild sweep events
python3 scripts/run_displacement.py - rebuild displacement events
python3 scripts/run_mss.py - rebuild MSS structure events
python3 scripts/run_fvg.py - rebuild FVG zones
python3 scripts/run_trading_plans.py - generate today's structured BTC/ETH trading plans before signal generation; active plans require a Draw and at least one Invalidation
python3 scripts/run_trade_plan_candidates.py - watch the active trading plan for the first executable candidate model: OSOK/Daily Range context, Sweep, MSS, and FVG retrace; missing confirmations are reported instead of promoted to execution
python3 scripts/run_signals.py - generate bias and signals with structural target_plan selection; TP1 prefers IRL and TP2 prefers ERL, and signals are skipped when traceable structural targets are missing
python3 scripts/run_execution.py - run paper execution gating, require structural targets plus management rules for modeled signals, create an execution record, then auto-settle the latest execution for that signal
python3 scripts/run_execution_ticket.py - generate a human-confirmation execution ticket, run Risk Governor before ticket creation and before confirmation, and then create either paper or semi_auto execution only after a valid confirmation step
python3 scripts/run_live_adapter.py - exercise the live broker preparation layer for submit, reconcile, cancel_all, or kill_switch; every attempted live operation is audited and real submission stays blocked unless live trading is explicitly enabled and confirmed
python3 scripts/run_risk_governor.py - run the independent market-state and risk governor pre-execution gate
python3 scripts/run_backtest.py - run backtest as plan_replay by default, replay active-plan candidates from appearance to confirmation to entry/invalidation, and write target-source traceability plus management rules into result meta
python3 scripts/run_autonomous_cycle.py - run the unattended cycle: offline checks, database probe, readiness checks, then guarded pipeline when the database is available; prints Next actions for the next unattended step
python3 scripts/run_database_readiness.py - run read-only database readiness checks for connection, schema, seed data, and candle availability; prints next_actions and action_plan in JSON mode
.venv/bin/python scripts/run_trading_delivery_rehearsal.py - run the P0 trading delivery rehearsal as one scripted flow: checklist drift check, historical repair decision, readiness/auto-backfill, guarded pipeline, deploy/health-check, and post-deploy observe
.venv/bin/python scripts/run_offline_checks.py - run the broader offline check suite, including the M35 acceptance posture through the Training Case acceptance manifest for focused milestone test suites, the Training Case package smoke registered as training_case_package, trading delivery checklist drift guard registered as trading_delivery_readiness, trading workbench syntax validation as dashboard_js_syntax, training console syntax validation as training_console_js_syntax, and dashboard and public docs privacy smoke coverage; this public validation runs as offline checks without external services
.venv/bin/python scripts/check_trading_delivery_readiness.py - verify README.md and RUNBOOK.md still expose the locked trading delivery checklist for preflight, deploy, observe, and rollback without relying on chat memory
.venv/bin/python scripts/run_training_case_package.py --help - show the offline package smoke help for public package import/export behavior, public export/public output semantics, and stable public error codes for invalid input paths and invalid output targets
.venv/bin/python scripts/run_training_case_package.py --input tests/fixtures/training_case_package_m6.json --output /tmp/training_case_public_export.json - run the Training Case offline package smoke, an import/export smoke with local files, and write public output as the public export in the public package shape; exported public JSON omits internal solution details
.venv/bin/python scripts/run_training_case_generator.py --help - show the offline prototype generator help for read-only Training case draft generation from trading-derived seed events
.venv/bin/python scripts/run_training_case_generator.py --input tests/fixtures/training_case_generator_seed_v1.json --output /tmp/generated_training_case_drafts.json - run the offline Training case generator prototype and write a public draft package without touching Training database tables
.venv/bin/python scripts/run_historical_compatibility_repair.py - scan for older stored signal/FVG/MSS/liquidity compatibility issues in the connected database without applying changes
.venv/bin/python scripts/run_historical_compatibility_repair.py --apply-safe-repairs - apply the safe historical compatibility repairs for signal invalidations, MSS/FVG meta backfills, and liquidity safe merges limited to unreferenced supersets plus exact duplicate rows with identical sweep events; needs_sweep_reference_migration and keep_version_history groups still require manual review
.venv/bin/python scripts/run_historical_compatibility_repair.py --apply-liquidity-reference-migrations - apply executable liquidity reference migrations for exact duplicate pools with nonidentical sweep events; duplicate sweep timestamp conflicts are blocked instead of auto-merged
python3 scripts/run_migration_static_checks.py - check migration order and required core schema without connecting to the database
python3 scripts/run_script_static_checks.py - check automation scripts for unsafe ambient environment fallback patterns and repo-tracked secret literals
python3 scripts/run_secret_static_checks.py - scan git-tracked text files for high-confidence secret material without printing secret values
python3 scripts/run_replay_file.py <candles.json|candles.jsonl|candles.csv> - run offline no-lookahead candle replay without database access
python3 scripts/run_reports.py - build sample reports from current services
python3 scripts/run_api_server.py - start a lightweight HTTP API server on 127.0.0.1:8000; / serves the trading workbench and /training serves the training console
python3 scripts/run_pipeline.py - run the current end-to-end closed loop and print a structured summary

Optional report environment variables used by run_reports.py:

REPORTS_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable sample report output

Optional utility JSON output variables:

CHECK_DB_OUTPUT_JSON
CHECK_DB_CONNECTION_OUTPUT_JSON
HISTORICAL_REPAIR_OUTPUT_JSON
MIGRATION_CHECKS_OUTPUT_JSON
SCRIPT_CHECKS_OUTPUT_JSON
SHOW_MODELS_OUTPUT_JSON
RISK_GOVERNOR_OUTPUT_JSON
TRADE_PLAN_CANDIDATES_OUTPUT_JSON
TRADING_JOURNAL_OUTPUT_JSON
TRADING_PLANS_OUTPUT_JSON
TRADING_REHEARSAL_OUTPUT_JSON
LIVE_ADAPTER_OUTPUT_JSON
BACKTEST_OUTPUT_JSON
EXECUTION_TICKET_OUTPUT_JSON

Optional structure script JSON output variables:

SESSION_GENERATOR_OUTPUT_JSON
SWINGS_OUTPUT_JSON
RANGES_OUTPUT_JSON
LIQUIDITY_OUTPUT_JSON
SWEEPS_OUTPUT_JSON
DISPLACEMENT_OUTPUT_JSON
MSS_OUTPUT_JSON
FVG_OUTPUT_JSON

Optional backtest cost environment variables used by run_backtest.py and run_pipeline.py:

BACKTEST_FEE_RATE
BACKTEST_SLIPPAGE_RATE
BACKTEST_FIXED_COST_R

Optional replay-backed backtest mode variables used by run_backtest.py and run_pipeline.py:

BACKTEST_MODE - supported values: entry_then_outcome or replay_no_lookahead
BACKTEST_REPLAY_LEFT_BARS
BACKTEST_REPLAY_RIGHT_BARS

Optional backtest automation gate used by run_backtest.py and run_pipeline.py:

BACKTEST_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable run_backtest.py output
BACKTEST_REQUIRE_QUALITY_GATE - set to 1, true, yes, or on to exit with code 2 when the backtest quality gate fails
BACKTEST_MIN_TOTAL_SIGNALS
BACKTEST_MIN_EXECUTED_SIGNALS
BACKTEST_MIN_EXPECTANCY_R
BACKTEST_MIN_PROFIT_FACTOR_R
BACKTEST_MAX_DRAWDOWN_R
BACKTEST_MAX_CONSECUTIVE_LOSSES

Optional pipeline environment variables used by run_pipeline.py:

PIPELINE_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable pipeline error output

Optional replay environment variables used by run_replay_file.py:

OFFLINE_REPLAY_FILE - optional candle file path used by run_offline_checks.py to include replay validation
OFFLINE_CHECKS_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable offline check summary output
REPLAY_LEFT_BARS
REPLAY_RIGHT_BARS
REPLAY_MIN_CANDLES
REPLAY_MIN_SWINGS
REPLAY_MAX_GAP_MINUTES
REPLAY_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable replay summary output

Example offline replay gate:

OFFLINE_REPLAY_FILE=tests/fixtures/offline_replay_sample.jsonl \
REPLAY_LEFT_BARS=1 REPLAY_RIGHT_BARS=1 \
REPLAY_MIN_CANDLES=5 REPLAY_MIN_SWINGS=2 \
.venv/bin/python scripts/run_offline_checks.py

Optional OKX backfill environment variables used by run_okx_backfill.py:

OKX_BACKFILL_SYMBOL - defaults to BTC-USDT
OKX_BACKFILL_INST_ID - defaults to <symbol>-SWAP
OKX_BACKFILL_PAGES - defaults to 20
OKX_BACKFILL_LIMIT - defaults to 100
OKX_BACKFILL_TIMEFRAMES - defaults to 1m,5m,15m
OKX_BACKFILL_OUTPUT_JSON - emit a machine-readable backfill summary

Optional autonomous cycle environment variables used by run_autonomous_cycle.py:

AUTONOMOUS_OUTPUT_JSON - emit a machine-readable cycle summary
AUTONOMOUS_REQUIRE_DB - return code 2 when the database is unavailable
AUTONOMOUS_RUN_PIPELINE - set to 0 to stop after a successful database probe
AUTONOMOUS_MAX_DB_PROBES - number of database probe attempts before stopping
AUTONOMOUS_PROBE_INTERVAL_SECONDS - sleep interval between database probe attempts
AUTONOMOUS_REQUIRE_SCHEMA - enabled by default; set to 0 to skip core table readiness checks before the guarded pipeline
AUTONOMOUS_APPLY_MIGRATIONS - enabled by default; when schema is not ready, run migrations, recheck schema, then continue
AUTONOMOUS_REQUIRE_DATA_READINESS - enabled by default; set to 0 to skip read-only seed/candle readiness before the guarded pipeline
AUTONOMOUS_BACKFILL_CANDLES - enabled by default; when readiness only lacks candles, run OKX backfill, recheck readiness, then continue
The guarded pipeline step defaults BACKTEST_REQUIRE_QUALITY_GATE=1 and PIPELINE_OUTPUT_JSON=1 unless explicitly overridden

Current autonomous modes emit machine-readable next_actions and action_plan, including:

waiting_for_database -> wait_for_database_recovery
database_schema_not_ready -> run_migrations_before_pipeline
migration_failed -> inspect_migration_output_and_schema
database_readiness_failed -> the readiness script's own next action, such as run_okx_backfill_or_load_replay_sample
backfill_failed -> inspect_backfill_output_and_data_readiness
pipeline_failed -> inspect_pipeline_output_and_quality_gates

Each action_plan item includes the action name, suggested command, description, and requires_database.

Optional database readiness environment variables used by run_database_readiness.py:

READINESS_OUTPUT_JSON
READINESS_REQUIRED_VENUES
READINESS_REQUIRED_INSTRUMENTS
READINESS_CANDLE_INSTRUMENT_ID
READINESS_CANDLE_TIMEFRAME
READINESS_MIN_CANDLES - defaults to 50
READINESS_MIN_CANDLE_SPAN_MINUTES - defaults to 60
READINESS_MAX_CANDLE_AGE_MINUTES - defaults to 240

Optional account risk environment variables used by run_execution.py and run_pipeline.py:

EXECUTION_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable run_execution.py output
EXECUTION_SETTLEMENT_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable run_execution_settlement.py output
EXECUTION_KILL_SWITCH
EXECUTION_DAILY_PNL_PCT
EXECUTION_CONSECUTIVE_LOSSES
EXECUTION_OPEN_POSITIONS
EXECUTION_SAME_SIDE_OPEN_POSITIONS
EXECUTION_MAX_DAILY_LOSS_PCT
EXECUTION_MAX_CONSECUTIVE_LOSSES
EXECUTION_MAX_OPEN_POSITIONS
EXECUTION_MAX_SAME_SIDE_POSITIONS

Optional market-state and risk governor environment variables used by run_risk_governor.py:

RISK_GOVERNOR_OUTPUT_JSON
RISK_INSTRUMENT_ID
RISK_TIMEFRAME
RISK_EXECUTION_MODE
MARKET_STATE_MAX_CANDLE_AGE_SECONDS
NEWS_WINDOW_ACTIVE
NEWS_WINDOW_ID
NEWS_WINDOW_MINUTES_BEFORE
NEWS_WINDOW_MINUTES_AFTER
OPENING_VOLATILITY_GUARD_MINUTES
BLOCK_OPENING_VOLATILITY

Optional signal quality environment variables used by run_signals.py and run_pipeline.py:

SIGNALS_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable run_signals.py output
SIGNAL_EVIDENCE_MODE - strict by default, or minimal to allow the older reduced evidence set

Optional trading plan environment variables used by run_trading_plans.py:

TRADING_PLANS_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable plan output
TRADING_PLAN_SYMBOLS - comma-separated symbols; defaults to BTC-USDT,ETH-USDT
TRADING_PLAN_BIAS_TIMEFRAME
TRADING_PLAN_SETUP_TIMEFRAME
TRADING_PLAN_TRIGGER_TIMEFRAME

Optional backtest replay environment variables used by run_backtest.py:

BACKTEST_SUBJECT_TYPE - defaults to plan_replay; use signal only when you intentionally want the legacy signal-only backtest
BACKTEST_TRADING_PLAN_ID - optional plan scope for replay
BACKTEST_CANDIDATE_MODEL_CODE - optional candidate model filter such as OSOK_DAILY_RANGE_SWEEP_MSS_FVG_RETRACE

Optional trade plan candidate environment variables used by run_trade_plan_candidates.py:

TRADE_PLAN_CANDIDATES_OUTPUT_JSON - set to 1, true, yes, or on for machine-readable candidate output
TRADE_PLAN_CANDIDATE_SYMBOLS - comma-separated symbols; defaults to BTC-USDT,ETH-USDT
TRADE_PLAN_CANDIDATE_SETUP_TIMEFRAME
TRADE_PLAN_CANDIDATE_TRIGGER_TIMEFRAME

Lightweight API helpers

The src/api/ package exposes internal query helpers and HTTP routes for:

GET /health
GET /candles?instrument_id=1&timeframe=1m&limit=100
GET /sessions?instrument_id=1&limit=20
GET /trading-plans?instrument_id=1&limit=20
GET /trade-plan-candidates?instrument_id=1&limit=20
GET /signals?instrument_id=1&limit=20
GET /executions?instrument_id=1&limit=20
GET /execution-tickets?instrument_id=1&limit=20
GET /live-operation-audits?execution_ticket_id=1&limit=20
GET /backtests?instrument_id=1&limit=10

Open / in the browser for the minimal dashboard, which now includes health, candles, signals, sessions, executions, and backtests.

Validation

Unit tests:

python3 -m unittest discover -s tests/unit -p "test_*.py"