Investment Automation CLI Platform for Brokerage Account Management

By Amar Kumar

A proposed investment automation CLI platform for teams that need to manage brokerage accounts programmatically — not through ad-hoc scripts or spreadsheet-driven workflows. The architecture would center on a scriptable command-line tool and supporting services that integrate with brokerage, market-data, and banking APIs; execute algorithmic strategies with idempotent order routing; enforce pre-trade risk controls; and emit audit-grade logs suitable for systems that move real money.

Proposed outcome: One CLI-driven platform where operators and cron jobs can sync positions, submit orders, run strategies, replay historical sessions, and inspect failures — with every outbound trade keyed by a client order ID, every API call traced, and risk gates that block bad orders before they reach the broker.

Scenario

This brief describes a proposed solution — not a delivered engagement. It maps a recurring pattern: a small engineering team building foundational automation for investment accounts where correctness, security, and unattended operation are non-negotiable.

• Operator profile: Senior software engineer with CLI and API integration experience; high ownership over architecture, production code, and runbooks; async-friendly remote team with short feedback loops
• Accounts: One or more brokerage accounts (REST/WebSocket APIs; FIX optional for institutional brokers); optional banking API for cash transfers and settlement visibility
• Strategies: Rules-based rebalancing, scheduled DCA, signal-driven entries/exits, or portfolio drift correction — not HFT; latency targets in seconds, not microseconds
• Runtime: CLI for manual ops and CI; long-running daemon or scheduled jobs for market-hours automation; paper-trading environment before live capital
• Compliance posture: Audit trails, least-privilege API keys, encrypted secrets, and explicit human approval gates for large or novel order types

Problem

Managing investment accounts through broker UIs and one-off Python notebooks does not survive production. Common failure modes when teams stitch together scripts instead of a platform:

• Duplicate orders — cron retries or network timeouts resubmit the same trade; without idempotency keys the broker fills twice
• Stale position state — local cache diverges from broker ledger after partial fills, corporate actions, or manual trades in the UI
• Silent API failures — rate limits and 5xx responses swallowed; strategies continue on bad data
• No pre-trade risk gate — fat-finger quantity, wrong symbol, or concentration breach reaches the market before anyone notices
• Unscriptable ops — no stable subcommands, exit codes, or JSON output; on-call cannot safely run recovery steps under pressure
• Weak audit trail — unstructured logs, no correlation IDs, impossible to reconstruct who submitted what and why for compliance review
• Backtest/live drift — strategy tested against CSV exports with different fill assumptions than live REST order types

Systems that move real money need transactional correctness, clear failure modes, and observability from day one — not bolted on after the first incident.

Requirements

Functional

• CLI shell — subcommands for sync, order, strategy run, backtest, status; config files; machine-readable JSON output; documented exit codes
• Brokerage adapters — unified interface over REST/WebSocket (and FIX where required); OAuth, API keys, signed requests; pagination and rate-limit handling
• Market data ingest — quotes, bars, and corporate-action hooks; WebSocket reconnect with sequence recovery
• Order management — submit, amend, cancel; track lifecycle (pending → partial → filled → rejected); client order ID idempotency
• Position & portfolio sync — reconcile local ledger to broker truth; detect drift and surface discrepancies
• Strategy engine — pluggable strategies with explicit inputs (positions, signals, config) and outputs (intended orders)
• Risk controls — max order size, max daily notional, symbol allowlist, concentration limits, market-hours guard
• Backtesting & replay — event-driven simulation against historical bars/ticks; same strategy code path as live with simulated fills
• Paper trading mode — full pipeline against broker sandbox or internal simulator before live keys

Non-functional

• Idempotency — every outbound order keyed; safe retries without double execution
• Concurrency — bounded worker pools; no unbounded goroutines or threads on WebSocket fan-out
• Secrets — vault or cloud secret manager; no credentials in config repos; rotation runbook
• Encryption — TLS in transit; encrypted DB at rest for order and audit tables
• Observability — structured JSON logs, Prometheus metrics, alert hooks; correlation ID per CLI invocation
• Test coverage — unit tests for risk math; integration tests against broker sandboxes; replay tests on fixed historical fixtures
• Uptime — daemon health checks; graceful shutdown; crash recovery from persisted intent queue

Architecture

Four layers: CLI & config (operator entry), domain services (OMS, portfolio, strategy), adapters (broker, market data, banking), and persistence & observability (Postgres ledger, metrics, audit log).

flowchart TB classDef cli fill:#ede9fe,stroke:#7c3aed,color:#5b21b6 classDef core fill:#dbeafe,stroke:#2563eb,color:#1e3a8a classDef adapter fill:#ccfbf1,stroke:#0d9488,color:#115e59 classDef store fill:#fef3c7,stroke:#d97706,color:#92400e classDef ext fill:#f8fafc,stroke:#475569,color:#334155 CLI["CLI invctl\nsubcommands + JSON output"]:::cli CFG["Config + secrets\nvault / env"]:::cli CRON["Scheduler / daemon\nmarket-hours jobs"]:::cli STRAT["Strategy engine\nsignals → intents"]:::core RISK["Risk gate\npre-trade checks"]:::core OMS["Order manager\nidempotent routing"]:::core PORT["Portfolio sync\nreconciliation"]:::core BT["Backtest / replay\nhistorical feed"]:::core BROKER["Brokerage adapter\nREST · WebSocket · FIX"]:::adapter MDATA["Market data adapter\nbars · quotes · WS"]:::adapter BANK["Banking adapter\ncash · transfers"]:::adapter PG["Postgres\norders · positions · audit"]:::store MET["Metrics + logs\nPrometheus · JSON"]:::store BAPI["Broker API"]:::ext MAPI["Market data feed"]:::ext CLI --> STRAT CLI --> OMS CLI --> PORT CLI --> BT CRON --> STRAT CFG --> CLI STRAT --> RISK RISK --> OMS OMS --> BROKER PORT --> BROKER BT --> STRAT BROKER --> BAPI MDATA --> MAPI STRAT --> MDATA OMS --> PG PORT --> PG OMS --> MET BANK --> BAPI

Platform architecture — CLI and scheduler feed a strategy → risk → OMS pipeline; adapters isolate broker-specific APIs; Postgres holds the canonical order and audit ledger

sequenceDiagram autonumber participant Op as Operator / cron participant CLI as invctl CLI participant Risk as Risk gate participant OMS as Order manager participant DB as Postgres ledger participant Broker as Brokerage adapter participant API as Broker API Op->>CLI: invctl order submit --client-id X CLI->>DB: lookup client_order_id X alt already submitted DB->>CLI: existing order row CLI->>Op: 200 OK (idempotent replay) else new intent CLI->>Risk: validate size, symbol, limits alt risk reject Risk->>CLI: reject reason CLI->>Op: exit 2 (blocked) else risk pass Risk->>OMS: approved intent OMS->>DB: insert pending (client_order_id X) OMS->>Broker: submit order Broker->>API: POST /orders API-->>Broker: ack + broker_order_id Broker-->>OMS: accepted OMS->>DB: update status=accepted OMS-->>CLI: order record CLI-->>Op: JSON + exit 0 end end

Order submission — client order ID checked before risk and broker call; retries return the existing record instead of duplicating fills

End-to-end flow

Typical automation cycle — positions and quotes refreshed, strategy emits intents, risk approves or blocks, OMS routes to broker, portfolio sync confirms ledger match

Recommended stack

Recommendation: Go for the CLI, OMS, and adapters (single static binary, strong concurrency, excellent CLI ecosystem); Postgres for the order and audit ledger; Python optional for quant research notebooks that export strategy configs — not in the hot execution path.

Why Go over Python for the execution path? Python is fine for research, but production CLIs that run unattended benefit from Go's compile-time checks, single-binary deploy, and predictable concurrency. Teams already strong in Python can keep notebooks for signal research while the hot path stays in Go.

Why Postgres over Redis-only? Order state needs durable ACID writes and queryable audit history. Redis can cache quotes; it should not be the sole source of truth for submitted orders.

Example CLI surface (proposed invctl command tree):

invctl sync positions --account default --json
invctl sync quotes --symbols AAPL,MSFT,VTI
invctl order submit --client-id rebalance-20260706-001 \
  --symbol VTI --qty 10 --side buy --type limit --limit-price 245.50
invctl strategy run --name drift-rebalance --dry-run
invctl strategy run --name drift-rebalance --live
invctl backtest run --strategy drift-rebalance --from 2024-01-01 --to 2025-12-31
invctl status orders --since 24h --json

Idempotency guard in the order manager (simplified Go pattern):

func (o *OrderManager) Submit(ctx context.Context, intent OrderIntent) (*Order, error) {
    existing, err := o.store.GetByClientOrderID(ctx, intent.ClientOrderID)
    if err == nil && existing != nil {
        return existing, nil // safe retry — do not re-hit broker
    }
    if err := o.risk.Check(ctx, intent); err != nil {
        return nil, fmt.Errorf("risk rejected: %w", err)
    }
    order := &Order{ClientOrderID: intent.ClientOrderID, Status: StatusPending}
    if err := o.store.InsertPending(ctx, order); err != nil {
        return nil, err
    }
    ack, err := o.broker.Submit(ctx, intent)
    if err != nil {
        _ = o.store.MarkFailed(ctx, order.ID, err)
        return nil, err
    }
    return o.store.MarkAccepted(ctx, order.ID, ack.BrokerOrderID)
}

Component design

1 — CLI shell (invctl)

• Input: subcommand, flags, config file (~/.config/invctl/config.yaml), environment overrides
• Output: human tables or --json for scripting; exit codes: 0 success, 1 runtime error, 2 risk/policy block, 3 usage error
• QA gate: golden-file tests for JSON output; shell completion generated in CI

2 — Brokerage adapter

• Input: normalized order intents, account ID, auth credentials from vault
• Output: broker order IDs, fill events, position snapshots, cash balances
• Behaviors: exponential backoff on 429/5xx; respect Retry-After; paginate list endpoints; WebSocket order updates with reconnect
• QA gate: integration tests against broker paper/sandbox; recorded HTTP fixtures for CI

3 — Market data adapter

• Input: symbol list, bar interval, optional real-time subscription
• Output: normalized OHLCV bars, last quote, corporate-action flags
• Behaviors: stale-data detection; clock skew guard; separate REST backfill from WS stream

4 — Strategy engine

• Input: current positions, target weights or signals, strategy config YAML
• Output: list of order intents (no direct broker calls — OMS owns submission)
• Modes: --dry-run (log intents only), paper, live
• QA gate: unit tests on position math; backtest parity check vs live dry-run on same fixture day

5 — Risk gate

• Checks: symbol allowlist, max single-order notional, max daily notional, max position concentration, market-hours window, min cash buffer
• Output: pass or structured reject reason logged to audit table
• Override: --force flag requires explicit env token for break-glass (logged)

6 — Order manager (OMS)

• Input: risk-approved intents with client order IDs
• Output: durable order rows; broker lifecycle updates (partial fill, cancel, reject)
• Behaviors: idempotent submit; outbox pattern for at-least-once delivery to broker; reconcile open orders on startup

7 — Portfolio sync & reconciliation

• Input: broker position API, local ledger
• Output: drift report; auto-heal local cache or alert on mismatch above threshold
• Cadence: after every strategy run and on hourly cron

8 — Backtest & replay engine

• Input: historical bars (Parquet), strategy config, simulated fill model (slippage, partial fills)
• Output: equity curve, trade log, risk metrics (max drawdown, Sharpe — indicative, not investment advice)
• Replay mode: feed recorded broker HTTP responses through adapter for integration regression tests

Implementation plan

Reporting & ops

Ops cadence would include a daily pre-market check (positions synced, cron healthy, no stale open orders), a weekly review of risk rejects and drift events, and a monthly credential rotation drill. On-call pages only for failed scheduled runs, sustained broker outages, or reconciliation drift above configured limits — not per-order notifications.

Proposed deliverables

Following the phased plan, a build would ship these artifacts:

• invctl CLI binary with subcommands, JSON output, shell completion, and documented exit codes
• Brokerage adapter (REST/WebSocket) with paper and live modes, rate-limit handling, and integration test suite
• Postgres schema: orders, fills, positions, audit log, strategy run history
• Order manager with client-order-id idempotency and outbox-based submit retry
• Risk gate module with configurable limits and break-glass override logging
• Reference strategy (drift rebalance) with dry-run, paper, and live paths
• Backtest and replay CLI with Parquet fixtures and CI regression tests
• Prometheus metrics, Grafana dashboards, and alert rule set
• Architecture document, threat model, CLI reference, and operator runbooks
• Terraform (or equivalent) for execution host, RDS, secrets, and IAM least-privilege roles

Effort estimate

Indicative effort for phases 1–6 (assumes one primary brokerage API, REST-first, one reference strategy, paper sandbox available):

At 30–40 hrs/week of focused engineering, delivery would land in roughly 12–16 weeks. Milestone-based phases de-risk capital exposure — read-only sync and paper trading validate adapters before live order routing; backtest parity gates strategy promotion.

Glossary