TickDB API development tutorials, WebSocket integration and SDK examples
Every trading system eventually confronts the same uncomfortable truth: its data lives in someone else's cloud. Between market open and market close, APIs are queried, dashboards refresh, and strategi
The CFO said everything was fine. The transcript said otherwise. Three hours after the Q3 earnings call, a junior analyst noticed a pattern: the word "challenging" appeared seven times — up from two i
--- "Here's the uncomfortable truth about market data APIs: every vendor has a ceiling, and the ceiling is usually money." When developers evaluate market data providers for US equities, they often en
--- At 3:47 AM on a Tuesday, a quant researcher named Wei watched his live P&L flip negative. The strategy had backtested beautifully — 34% annualized return, Sharpe of 2.1, max drawdown under 8%. Twe
--- Every quant researcher eventually hits the same wall. You are designing a mean-reversion strategy. You need 10 years of minute-level bars for S&P 500 components. You write a simple script to fetch
"Price is the effect. Usage patterns are the cause." Three months after deploying his momentum strategy, a systematic trader named Marcus received a billing alert that made him pause. His monthly API
--- The market closes at 4:00 PM ET. By 4:01, you're already running your end-of-day analysis — except you're waiting for a colleague to upload a CSV, or your data vendor's batch job to finish, or som
The moment your backtest stretches from 10,000 bars to 10 million, you discover a brutal truth: most technical indicator implementations are designed for demonstration, not production. I learned this
--- > "The most expensive line item in a quantitative trading system is rarely the servers or the data — it's the legal liability that appears when someone realizes the data license doesn't cover live
The backtest showed 2.4 Sharpe. Maximum drawdown was a comfortable −6.3%. You ran it on three years of daily data across four market regimes. Everything looks clean. Then you fund the account with $50
--- The 3 AM Wake-Up Call Your phone buzzes at 3:14 AM. The trading system reported an anomaly during the Tokyo session. You SSH into the server, scroll through logs that look like this: Which order?
--- Every security incident starts with a small, seemingly innocent decision. In 2019, a popular GitHub repository was found leaking AWS credentials to a third-party logging service. The cause? A deve
--- The Sunday Night Realization It's Sunday at 11 PM. You've just finished a grueling week at your day job. You open your laptop to check on the trading system you spent all of Saturday building. You
--- The first time your backtest grinds to a halt at 3 AM, you will not blame the market. You will blame yourself for not handling the rate limit correctly. It starts innocently enough. Your strategy
--- "Every time a developer uses WebSocket to pull historical klines, a quant weeps." That's not a joke. It's a pattern I've seen across dozens of integration attempts: engineers reaching for real-tim
--- You have a trading strategy. You have market knowledge. You have $100 per month. The question is not whether you can afford to run a quantitative strategy. The question is how to allocate that $10
The price does not exist between the closing bell and the opening bell. Yet for three months, a systematic futures trader in Shanghai watched his mean-reversion strategy crush backtests—Sharpe ratio o
You have built a beautiful trading system. The backtest looks promising. The strategy logic is sound. And then you hit the production wall — the part where the market data bill arrives. For individual
Every Sunday night, you set up a backtest. It runs for 47 minutes. You check the results Monday morning. It crashed at hour 3 because a data endpoint was down. The weekend is gone. You missed the sign
The first time a quant researcher runs a vectorized backtest against a 2020-style volatility spike and watches their Sharpe ratio collapse in live trading, they understand why the industry keeps circl