TickDB API development tutorials, WebSocket integration and SDK examples
--- The 3 AM Production Alert At 3:17 AM on a Tuesday, your monitoring dashboard fires an alert. The live trading signal system has gone silent. No new market data for the past 47 seconds — an eternit
The order book updated 3,400 times in 47 seconds. Your strategy did not fire once. This is not a bug in your alpha. It is a structural problem in how your system consumes market data. Every synchronou
--- Opening At 9:42 AM on March 18, 2020, the S&P 500 touched the day's circuit breaker threshold for the third time in two weeks. The CME Futures contract halted for 15 seconds—long enough for the or
--- When a junior analyst needs the current bid-ask spread on NVIDIA before an earnings call, the typical workflow looks like this: open a terminal, recall the correct API endpoint, write or copy a Py
--- You have a laptop, a dream, and maybe $50 a month to spend on infrastructure. This is the starting point for most quantitative traders who are building their first production system. They have ide
When I first built a momentum strategy in Python, I wrote it with pandas in an afternoon. Returns looked spectacular: 34% annualized, Sharpe of 2.1. I was ready to deploy. Then I tried to model limit
--- The Question Nobody Wants to Answer in Production You have 847 symbols on your watchlist. Your strategy demands sub-second updates across all of them. The documentation says TickDB's WebSocket sup
--- The first time I watched a production quant system fail, it wasn't because the strategy was wrong. The alpha model identified a statistical arbitrage opportunity with a theoretical edge of 8 basis
The packet hit the wire at 09:31:07.142 UTC. By 09:31:07.158, the single WebSocket connection managing 127 asset subscriptions had buffered 847 order book updates. The system's event loop stalled for
When a developer first integrates a market data API, a common reflex is to pick one protocol and use it everywhere. REST is familiar. WebSocket feels modern. Why not just pick a lane and stay there? T
--- "Ask your trading system anything about the market. Get answers, not error codes." For the past three years, building a quant workflow meant writing Python scripts, debugging API integrations, and
--- "Two hours into a backtest run, your WebSocket connection drops for exactly 4 minutes and 37 seconds. When you reconnect, the stream picks up at the current price — but the gap is gone. You have n
--- Every developer has encountered a 429 error at some point. The server says "too many requests," you wait a few seconds, and you retry. It works. But when you encounter a 3001 error code, the first
--- Every algorithmic trading platform shows the same story in its marketing: a gleaming equity curve, a Sharpe ratio above 2.0, and a strategy that "crushed the market." Scroll down past the headline
--- Every quant engineer reaches the same inflection point. The strategy is solid. The backtest looks promising. Then the live deployment begins—and the system buckles. The order book updates arrive a
--- Price is the effect. The absence of price is also a signal. On March 9, 2020, the Dow Jones Industrial Average opened down 1,800 points and circuit breakers triggered within minutes. During the 15
One data outage costs more than six months of cloud fees. At 9:32 AM on a Tuesday, the senior researcher on a three-person quant team discovered that his backtest had been running on stale data for th
--- Why "Adjusted Close" Is a Lie (And What to Do About It) On July 15, 2020, Apple executed a 4-for-1 stock split. The closing price on July 13 was $387.31. On July 14, it opened at $95.89. If you ra
--- The Bill That Broke the Backtest Three months into production, the algorithm was working. Sharpe ratio held at 1.42. Max drawdown sat at −8.3%. The backtest looked clean across four years of tick
The first time your Python backtest stalls for 47 minutes on a dataset that should take 4, you realize the problem isn't your strategy. It is the language. In quantitative development, the transition