Algorithmic Trading & Quantitative Research
Alpha research, rigorous backtesting and production deploy for trading rooms, funds and sophisticated private traders. Consulting under NDA.
Institutional-grade quantitative research and algorithmic trading systems across crypto, equities, indices, commodities, forex and derivatives. From advanced cycle analysis (DSP, adaptive filters, spectral analysis, Hurst theory) to Monte Carlo simulations, through to the deployment of automated trading systems on retail brokers, prime brokers and proprietary infrastructure — with integrated risk management, stress testing and real-time monitoring.
- ✓Rigorous backtesting without lookahead bias and without repainting — results that hold out-of-sample
- ✓Full ownership of strategies, code and data: no black boxes, no SaaS provider that knows your alpha
- ✓Deploy on retail brokers (IB, Binance, Bybit) or prime brokers with the same codebase — no refactor when you scale
- ✓Integrated risk management: max drawdown, kill-switch, adaptive position sizing, Monte Carlo stress tests
Type, timeline, pricing and stack
| Project type | Trading systems, backtesting frameworks, alpha research, low-latency infrastructure, risk dashboards |
|---|---|
| Typical timeline | 6 to 16 weeks depending on strategy complexity and multi-asset scope |
| Price range | Under NDA |
| Typical stack | Python, Polars, NumPy, Numba, SciPy, Statsmodels, CCXT, PostgreSQL, C#/.NET |
Related case studies
CyclicalResearch
Advanced cyclical analysis platform for crypto. DSP, spectral analysis, regime detection and interactive dashboard. Proprietary.
Forecast Engine
Crypto price forecasting engine with Prophet models, seasonal decomposition and cross-validation. Proprietary.
Frequently asked questions
Why do you require an NDA before the quote?+
To discuss strategies, alpha sources and existing infrastructure with the level of detail needed for honest scoping. It's a safeguard for both sides: you protect IP, we work on real information instead of generic assumptions.
Who owns the strategies developed?+
You, 100%. Code, optimized parameters, signals, processed historical data — all yours, in your private repository, with no residual rights or royalties for us. Contractually documented.
How do you avoid lookahead bias and repainting in backtests?+
Absolute rule: never use future data to compute past values. Point-in-time correct pipelines, walk-forward validation, rigorous out-of-sample tests, randomized Monte Carlo on gaps and slippage. Every assumption documented.
Which brokers and exchanges do you deploy on?+
Experience across Interactive Brokers, Binance (spot + futures), Bybit, CQG, Rithmic, MetaTrader. REST/WebSocket and FIX connectivity for institutional brokers. Prime broker deploy evaluated case-by-case.
How do you handle operational risk management?+
Per-strategy limits (max position, max exposure), kill-switch on intraday max drawdown, circuit breaker on latency degradation, automatic broker vs internal P&L reconciliation, multi-channel alerting (email, Telegram, webhook).
Where do you source clean historical data?+
Multi-source: tick/bar from institutional brokers, Polygon, Binance archive, CCXT histories, premium vendors for equities. Documented cleaning pipeline (gap handling, split adjustment, timezone-aware, no survivorship bias).
Can you integrate machine learning models into strategies?+
Yes, with discipline: point-in-time feature engineering, walk-forward validation, probability calibration, rigorous backtest. ML is useful when it improves the baseline, not because it's ML — we decline projects where the client wants ML as marketing with no baseline.
How we work
Our agency process in 5 steps
- 1
Discovery & Spec
We analyze goals, constraints and KPIs together with the client's product team. We define scope, deliverables and acceptance criteria before estimating — no estimates on fuzzy scope.
- 2
Architecture
We design the data model, external integrations and contracts between modules. No code before the map is clear: you save weeks of downstream refactor.
- 3
Iterative development
Short cycles with weekly client demos, dedicated branch per feature, continuous code review. Every release is production-ready, not a throwaway prototype.
- 4
Review & test
Automated tests, QA checklist, security and accessibility audit before release. No surprises in production, no incidents in the first 48 hours.
- 5
Deploy & handover
Production deploy, operational documentation and training for your internal team for full post-project autonomy. You can continue with us or hand off with no hidden dependencies.