A valid signal can still be a bad order
Loss limits, correlation, spread, news, portfolio heat, and existing exposure all affect whether execution is appropriate.
FIELD NOTES / FINTECH & CAPITAL MARKETS
The signal is easy.
The operating system is not.
Cadence turns trading signals into risk-controlled, auditable broker executions—connecting one strategy core to pre-trade gates, position sizing, order safety, lifecycle management, reconciliation, research, and real-time operational oversight.
FINTECH CASE STUDY
Cadence
Algorithmic trading.
Operational control.
Cadence / Trading command centerOPERATIONS EXPERIENCE
ONE OPERATING VIEWStrategy, exposure, risk, and system health.
Operators can see what is trading, what is blocked, what the broker reports, and what needs intervention.
Inspect the controls ↗SignalEvaluate riskExecute onceReconcile
DOMAIN
Algorithmic trading operations
WHAT WE BUILT
Execution + risk platform
OUR ROLE
Product, quant & full-stack engineering
01 / THE REAL ENGINEERING PROBLEM
A strategy can find a trade.
A platform must survive it.
Production trading is not only signal generation. Every decision must account for portfolio exposure, loss limits, execution costs, broker state, network uncertainty, position management, and the integrity of future strategy changes.
Cadence was engineered around two questions: before a trade, should the system be allowed to place it? After a trade, what happened—and does the platform’s state agree with the broker?
The platform operated against OANDA’s brokerage environment and processed more than 500 trades through execution, management, and reconciliation workflows.
A timeout can create duplicate exposure
The broker may accept an order even when the platform never receives the response, making blind retry unsafe.
Research can drift away from live logic
Separate strategy implementations make historical results describe software that is no longer actually trading.
02 / INSIDE THE OPERATIONS PLATFORM
Control before execution.
Evidence after it.
Explore the supplied interfaces for command oversight, risk enforcement, trading history, and system-state auditability.
A command center for the complete operating state.
Positions, exposure, breakers, reconciliation, data freshness, signal decisions, and alerts remain visible in one operational workspace.
THE CONTROLLED TRADE LIFECYCLE
Signal→Strategy→Rules→Risk gates→Size→Execute→Manage→Reconcile
03 / RISK & EXECUTION GOVERNANCE
Every order path.
One controlled chokepoint.
TradingView webhooks and the autonomous scanner converge on the same strategy and risk pipeline. No order-producing path bypasses shadow mode, position limits, idempotency, execution validation, or slippage protection.
Look under the hood ↘01
Evaluate the complete context
24 layered pre-trade checks across rules, portfolio gates, and execution economics
Size within hard boundaries
Allocation, streak, drawdown, session, and optional Kelly adjustments remain below absolute caps.
Reject and explain
Record the check, threshold, measured value, outcome, and evaluation duration.
02
Submit exactly once
Use a unique client identifier and resolve uncertain broker state before any retry.
03
Reconcile to broker truth
Combine the transaction stream with independent polling and idempotent persistence.
Risk infrastructure
If critical balance, exposure, or gate inputs cannot be trusted, trading is blocked.
Optional intelligence
If ML or sentiment becomes unavailable, it degrades to a neutral state instead of destabilizing execution.
Strategy promotion
Research may recommend a configuration; it cannot silently replace live strategy parameters.
04 / THE TECHNOLOGY & THE REASON
One strategy core.
Multiple layers of proof.
The architecture keeps research and live logic congruent, centralizes order authorization, treats the broker as the source of truth, and separates optional intelligence from the safety-critical execution path.
OPERATIONS TERMINAL
Next.js + React + WebSockets
A browser-based command surface brings live positions, risk, research, execution, reconciliation, and audit into one product.
Why this combination?
Real-time transport and tiered refresh patterns keep fast-changing state visible without turning every view into a polling storm.
TRADING SERVICES
Python + FastAPI
Strategy functions, risk services, execution, reconciliation, research, and APIs share a typed Python foundation.
Why one strategy language?
The same pure signal logic can run in historical simulation and live scanning, reducing research-to-production drift.
BROKER INTEGRATION
OANDA v20
REST execution, pricing, account data, and transaction streaming connect the platform to broker-side reality.
Why dual reconciliation?
A live event stream minimizes latency while 60-second polling independently protects correctness after missed events.
OPERATIONAL RECORD
PostgreSQL + isolated lab data
Trades, signals, decisions, settings, versions, and audits stay distinct from experimental research records.
Why isolate research?
Optimizer and backtest activity cannot contaminate production trading statistics or operational state.
VALIDATED INTELLIGENCE
XGBoost + FinBERT
Regime, anomaly, exit, and sentiment services add context while remaining outside the core reliability path.
Why validation-gated models?
A more sophisticated model is promoted only when it demonstrates improvement against the established baseline.
DELIVERY & HARDENING
GitHub Actions + 115+ tests
Automated delivery, health verification, restart drills, and regression coverage support operational reliability.
Why test failure behavior?
Financial controls matter most during restarts, timeouts, and degraded dependencies—not only on the happy path.
CONTROLLED EXECUTION ARCHITECTURE01 / SIGNALS 02 / STRATEGY 03 / DECISION 04 / EXECUTION 05 / BROKER POSITION LIFECYCLE BROKER TRUTH
TradingView + scanner
External alerts and five-minute internal scans converge.
Shared strategy core
The same functions support backtest and live scanning.
24 pre-trade checks
Rules, risk gates, execution costs, then dynamic sizing.
Single order gateway
Shadow control, idempotency, limits, and slippage bounds.
OANDA v20
Bracket orders, pricing, account data, and transaction events.
Manage the open trade
Breakeven, partial profit, trailing behavior, time stops, and news protection.
Reconcile continuously
Resumable stream, gap replay, independent polling, and idempotent updates.
The architecture and product narrative reproduce the supplied project documentation. Interface values are product screen data and are not presented as investment results or evidence of future performance.
Explore the complete documented stack
The delivered platform spans live operations, quantitative research, broker integration, machine learning, reporting, security, and cloud delivery.
- Frontend
- Next.js 16 · React 19 · TypeScript · Tailwind CSS · SWR · native WebSockets
- Backend
- Python 3.11 · FastAPI · uvicorn · Pydantic · 13 background services
- Broker & signals
- OANDA v20 REST + transaction stream · TradingView · Pine Script · internal scanner
- Research & ML
- pandas · NumPy · SciPy · scikit-learn · hmmlearn · XGBoost · FinBERT · PyTorch
- Data & AI
- PostgreSQL · SQLite WAL · Redis · Groq-hosted Llama · deterministic analytics
- Security & platform
- JWT · PBKDF2-SHA256 · login throttling · DigitalOcean · nginx · pm2 · GitHub Actions
Scale documented in the source
130+ REST routes · 31 dashboard panels · 24 layered pre-trade checks · 13 background services · 500+ trades processed · 115+ automated tests · more than three months against live market data
Responsible positioning
Cadence was engineered to make a deployed strategy operate through controlled, observable, testable, and auditable infrastructure. This case study does not claim or guarantee trading profitability, investment performance, or future results.
Technology and platform names identify the documented implementation, not partnerships or endorsements.
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