Algorithmic & AI Trading
Browse Topics
Data Vendors, Alternative Data and Cost‑Effective Feeds for FX Machine Learning
Compare FX data vendors, free & low‑cost feeds and alternative data (news, on‑chain) to build robust, cost‑efficient machine‑learning models for FX trading.
How to Automate Strategy Deployment: From Backtest to VPS to Live Execution
Step-by-step guide to move a strategy from backtesting to VPS and live execution — covering containerization, CI/CD, broker APIs, monitoring, and risk controls.
Version Control, CI/CD and Testing for Trading Bots — DevOps Best Practices
DevOps for trading bots: git workflows, CI/CD, unit & integration tests, reproducible backtests, model/artifact versioning, secure secrets, monitoring.
Model Risk Management for Retail Quants: Monitoring, Drift Detection and Retraining
Practical model risk guidance for retail quants: monitoring metrics, drift detection methods and cost‑aware retraining schedules to keep ML trading models robust.
Event‑Driven Backtesting: Simulating News, Central Bank Speeches & Flash Volatility
Simulate news, central‑bank speeches and flash volatility in backtests. Techniques for timestamping, slippage, market impact and execution realism and tests
Paper Trading Pitfalls: When a Winning Backtest Fails Live — and How to Fix It
Why paper trading/backtests often fail live—slippage, execution gaps, overfitting and drift. Practical fixes: realistic cost models, walk‑forward tests and continuous monitoring.
Backtesting Multi‑Asset, Multi‑Timeframe Strategies for FX–Crypto Pairs
Backtesting multi‑asset FX–crypto strategies: pick tick‑level & on‑chain feeds, model slippage/fees, run walk‑forward validation and stress tests.
Building a Robust Forex Backtest Pipeline with Live Data Feeds and Slippage Modeling
Build a production-grade FX backtest pipeline with tick feeds, FIX/WebSocket ingestion, realistic slippage models and walk‑forward validation & audit logs.