TickDB API development tutorials, WebSocket integration and SDK examples
The first version of our data pipeline required managing seven separate WebSocket connections, four vendor SDKs, and two polling loops. At 9:30 AM ET on a typical trading day, we were burning CPU on c
--- Picture this: it's 11 PM on a Saturday. You've spent six hours debugging a beautifully written backtest that showed 34% annualized returns. You run it live on Monday morning. By 10:15, your strate
In production trading systems, a single anomaly can expose the fragility of naive data pipelines. Consider this scenario: at 09:30:00 ET, a major macroeconomic announcement triggers a 47-millisecond b
"Price is the effect. The architecture is the cause." A quant team once told me they received a $4,200 monthly bill for what they assumed was "just a simple monitoring script." The script polled 200 s
The first time I built a trading system, I spent three weeks installing libraries before writing a single line of strategy code. I was not alone. The Python quantitative ecosystem is simultaneously it
"History is written by the winners — but backtests are written by whoever remembered to adjust for splits." Consider a quant researcher in 2020 who built a mean-reversion strategy on Tesla. They used
--- > "The server restarted at 3:47 AM. Your WebSocket dropped. You didn't notice until morning." That sentence has ended more than a few quantitative trading careers—or at least a few weekends. In li
--- 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