TickDB API development tutorials, WebSocket integration and SDK examples
A market-data request can look successful while quietly failing the system that depends on it. At 09:29:59, a trading process receives a quote, updates its order-flow estimate, and places an order. Th
At 02:13 on a research notebook, a backtest looks spectacular: a smooth equity curve, a Sharpe ratio above 2, and a maximum drawdown smaller than most index funds. Three hours later, the result disapp
Natural Language Market Queries: AI Agent Integration with TickDB A trader asks, “Show me the current order-book imbalance for Bitcoin on the five-minute chart,” and receives a precise answer within s
REST vs WebSocket: Choosing APIs for Market Data Workflows A trader opens a dashboard before the market opens, requests the last 2,000 candles, and receives a complete historical series. Thirty second
A strategy can survive a bad signal, a missing fill, and a temporary drawdown. It rarely survives the trader who decides the backtest was wrong. The problem usually appears at the worst possible momen
--- The Moment Your Strategy Falls Behind At 09:30:00.123 ET on a typical trading day, the flood arrives. A major macroeconomic announcement drops. The market moves. In the next 500 milliseconds, your
--- The Question Every Quant Developer Asks It usually starts the same way. You're three weeks into building a mean-reversion strategy on US equities. The OHLCV data looks clean. The backtest is singi
--- At 2:43 AM on a Tuesday, a personal quant trader in Shenzhen watched his strategy implode silently for six hours. No alerts fired. No connection errors appeared in the logs. The market data feed h
--- The Spread That Vanishes in 400 Milliseconds At 03:47 UTC on March 15, BTC/USD on Coinbase hit $67,234 while the same asset on Binance US traded at $67,198. A $36 spread. Thirty-two milliseconds l
--- The $340 Million Problem No One Talks About On May 6, 2010, the Dow Jones Industrial Average plunged nearly 1,000 points in minutes before recovering. Much of what traders saw that day was not rea
For eight months, a systematic trader I know ran his entire backtesting pipeline through a WebSocket connection. He justified it — correctly, he thought — because his data provider offered a WebSocket
--- "Python will lose you millions in latency. Not figuratively. Literally." I learned this the hard way during a 2023 volatility spike. My Python-based quote aggregation system was processing market
--- Opening The alert fired at 3:47 AM Eastern. AWS US-East-1, the region handling 60% of the world's cloud compute, was experiencing elevated error rates. Within minutes, the S3 control plane degrade
"Price is the effect. The order book is the cause." A quant researcher runs a mean-reversion strategy on three years of historical data. The strategy returns 23% annualized with a Sharpe of 1.8. They
You can write a REST API in your sleep. You understand loops, functions, and data structures. You have debugged code at 3 AM. But when someone mentions "quantitative trading," does your mind go blank?
In 2019, a systematic futures trader we know ran a mean-reversion strategy on five years of daily OHLCV data. The backtest showed a Sharpe ratio of 2.1. After six months live, the strategy bled 18% an
Every trading system eventually confronts the same architectural fork in the road: should I pull data with HTTP requests, or maintain a persistent connection? The answer seems simple on the surface. H
"Three AM. Your phone buzzes. A backtest crashed, and you have six hours before the market opens." The irony of quantitative trading is that the people who build automated systems often live the least
Every portfolio manager has faced this moment. Your strategy delivered 22% returns last year — an impressive number that looks stellar on a one-pager. But when a sophisticated investor asks the simple
"Connection reset by peer." Three words that have ended many a trading system's weekend. At 3:47 AM on a Saturday, while the engineer who deployed the system sleeps, the WebSocket connection silently