TickDB API development tutorials, WebSocket integration and SDK examples
--- The Problem Nobody Tells You About You write a beautiful mean-reversion strategy. It backtests well. You deploy it. And then, on a high-volatility day, your WebSocket feed delivers 50,000 ticks pe
--- For quant developers building systematic strategies on Chinese equities, the data decision is rarely simple. Tushare has long occupied the role of default choice for A-share researchers — a well-w
A production strategy blew up last week. Not because the alpha decayed — but because nobody remembered that the entry signal used a data feed that had quietly changed its timestamp format six months e
--- Network partitions happen. A fiber cut at your data center's upstream provider, a brief BGP flap, a momentary cloud infrastructure hiccup — these events last seconds or minutes. When your WebSocke
--- You pull your favorite stock's five-year daily OHLCV from a public data source. You run a moving average crossover backtest. The strategy returns 340% annualized. You are confident. You deploy cap
Every quant developer remembers the first time they opened PyPI and searched for "trading." The results were staggering: 847 packages with "trading" in the name. Pandas, NumPy, Backtrader, Zipline, Py
--- The Moment Everything Stopped At 10:02 AM ET on December 7, 2021, a routine configuration change in an AWS availability zone triggered a cascading failure across US-East-1. Within minutes, the new
"Price is not data. Price plus context is data. Price plus context plus history is a strategy." For quant traders and systems traders, this distinction matters more than it might first appear. A singl
The moment you try to query a decade of 1-minute OHLCV data for a single stock symbol — roughly 3.5 million rows — against a naive MySQL installation, you will watch a spinner for 90 seconds or longer
A trading system built on faulty latency assumptions does not fail dramatically. It fails quietly — slipping into a 200 ms disadvantage on every tick, compounding silently until the drawdown curve tel
In 2019, I spent three weeks building a backtesting engine from scratch. I wrote custom data loaders, rolled my own position tracking, and hand-rolled a naive portfolio p&l calculator. It worked. Then
--- "Price is the effect. The order book is the cause." Imagine this: your system is ingesting real-time NVIDIA options flow through a WebSocket stream. At 14:47:23 UTC, a network partition severs the
--- The Problem That Keeps Quant Teams Up at Night Imagine you are a quant researcher. You have spent three months building a mean-reversion strategy that looks spectacular on paper — 1.87 Sharpe rati
--- "Build it and they will come" works for software. It does not work for quantitative trading. In 2019, a talented backend engineer I know spent three months building a mean-reversion strategy on wh
At 3:47 AM Eastern Time on December 7, 2021, a routine deployment in AWS us-east-1 triggered a cascading failure that knocked out Elastic Load Balancing across the entire region for approximately thre
The $2,700 Mistake No One Talks About Your mean-reversion model had been humming along beautifully for eight months. Sharpe of 1.4. Maximum drawdown under 6%. Then NVIDIA announced a 10-for-1 split, a
A retail quant trader in Austin, Texas once showed me his backtest results. The strategy returned 31% annualized over three years with a Sharpe of 1.8. Clean equity curve, low drawdown. He was ready t
--- A junior quant once told me he spent $400 on premium futures data before realizing his strategy had negative expected value after commissions. He had optimized the wrong variable. The math is brut
--- "Price is the effect. The order book is the cause." — This principle guides every quant trader watching real-time market data. But what happens when the cause survives a server reboot, a deploymen
The invoice arrived at 8:47 AM on a Tuesday. The number was wrong — not catastrophically, but wrong enough that the finance team sent a Slack ping, and the engineering manager forwarded it with a sing