Our core service offering

Algorithmic Strategy Development & Backtesting Data Engineering & Predictive Modeling Deployment, Automation & MLOps Support

Our Core Offerings

Each of these flagship services delivers comprehensive, Python-powered expertise in algorithmic trading. Clients can choose one or combine multiple services to match their immediate needs and long-term goals.

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Algorithmic Strategy Development & Backtesting

A turnkey solution to design, prototype, and validate systematic trading strategies in Python. From idea to live‐ready concept, this service covers:

  • Strategy Formulation: Translate your trading thesis (momentum, mean-reversion, statistical arbitrage, cross-asset spreads, etc.) into precise entry/exit rules, risk parameters, and portfolio logic.
  • Custom Backtesting Engine: Build a lightweight, Python-based backtester (pandas/NumPy) or integrate with frameworks like Backtrader or Zipline. Includes realistic slippage and transaction-cost modeling, walk-forward testing, and trade‐level P&L reports.
  • Performance Analysis: Generate key metrics (Sharpe, Sortino, max drawdown, winning rate), equity‐curve visuals, and sensitivity/stress tests. Provide detailed Jupyter notebooks documenting assumptions, results, and next steps.

Value Proposition:

  • Rapid validation of alpha ideas without investing in expensive commercial platforms.
  • Complete transparency: you receive the Python code, parameter settings, and data preprocessing steps.
  • Actionable insights: know immediately whether a strategy meets your risk/return targets before moving to production.

Data Engineering & Predictive Modeling

An end-to-end service to ingest, cleanse, and transform market (and fundamental) data, then build predictive models that forecast price, volume, volatility, or execution signals. Key components include:

  • Historical Data Pipeline: Consolidate multi-year tick/bar data from CSV, API, or database sources. Parse, clean, adjust for corporate actions or data gaps, and store in Parquet/Delta format for fast retrieval.
  • Feature Engineering & Governance: Automate creation of time-series features (lags, rolling windows, Fourier terms, holiday flags) and maintain data quality via Great Expectations or Pandera checks. Optional feature store implementation (Feast) for live/​offline consistency.
  • Quantile Forecasting & ML Models: Train LightGBM quantile regression for probabilistic price/volume intervals (P05/P50/P95), CatBoost multivariate models, or PyTorch-Forecasting (Temporal Fusion Transformer) for longer horizons. Deliver back-test reports showing coverage, MAE, and calibration.
  • Scenario & Risk Metrics: Build Monte Carlo scenario generators (copulas, EVT tails) to quantify downside risk and aid position sizing under stress.

Value Proposition:

  • High-quality, analytics-ready data eliminates manual wrangling and ensures consistent feature sets across research and production.
  • Probabilistic forecasts allow traders to size bids/hedges around confidence intervals, reducing surprise outliers.
  • Scalable architecture ready to incorporate new data sources (alternative data, news sentiment, on-chain metrics).

Deployment, Automation & MLOps Support

A comprehensive service to take validated strategies and models into production, ensuring reliability, monitoring, and continuous improvement:

  • Trade Execution & Broker API Integration: Develop Python wrappers for REST or FIX APIs (Interactive Brokers, Alpaca, Binance, etc.), encapsulating order-type logic (market, limit, stop), slippage modeling, and circuit-breaker safeguards.
  • Serving Layer & Dashboard Deployment: Containerize models into FastAPI microservices (Docker/Helm) with sub-second latency. Build interactive dashboards (Streamlit, Panel, or custom React) so traders can query forecasts, risk metrics, and optimization outputs in real time.
  • CI/CD & Model Registry: Integrate MLflow (or BentoML) for experiment tracking, model versioning, and automated promotion from staging to production. Implement GitLab CI/GitHub Actions pipelines that run unit tests, quality checks, and deploy upon approval.
  • Monitoring & Drift Detection: Set up Prometheus/Grafana dashboards for model latency, throughput, and data drift (Evidently or custom PSI/KL divergence checks). Write Prefect or Airflow flows that automatically trigger retraining when drift thresholds are breached.
  • Disaster Recovery & Infrastructure as Code: Use Terraform or Pulumi to script infrastructure provisioning for cloud or on-prem environments. Implement automated fail-over scripts, cross-region data replication, and backup/restore procedures for critical components (databases, Kafka, model artifacts).
  • Hypercare & Ongoing Support: Offer 2–4 weeks of post-go-live hypercare, including 24/5 SLAs for incident response, bug fixes, and minor enhancements. Provide documentation and training to internal staff.

Value Proposition:

  • Guarantees that your strategies run reliably in production with minimal downtime.
  • Full visibility into model health and performance; drift alerts mean proactive retraining before P&L erosion.
  • Seamless integration with existing IT workflows (Docker, Kubernetes, Terraform), reducing friction with DevOps teams.