TickDB API development tutorials, WebSocket integration and SDK examples
--- 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
Price is the effect. The order book is the cause. At 9:30:00 AM ET on a typical trading day, Apple's order book sits in relative equilibrium — bid sizes roughly matching ask sizes, the spread compress
--- "Price is the effect. The order book is the cause." In systematic trading, this principle drives every decision. But what happens when your trading system requires market data that no standard API
--- The strategy looked perfect on paper. In 2021, a well-known hedge fund published a paper on a cross-sectional momentum strategy involving sector rotation and mean-reversion filters. The backtests
--- "China's A-share market is the world's second-largest by daily turnover — yet most Western quant tools treat it as a footnote." That asymmetry has real consequences. A quant researcher building a
--- You spent three years building a strategy that works. Your Sharpe ratio holds across bull markets, bear markets, and the 2022 rate-shock regime. You run the backtests yourself. You coded every sig
Every evening around 4:05 PM ET, when US equity markets have closed their regular session, a quiet problem begins. The day's 390 minutes of price action — captured in 1-minute OHLCV candles for every
--- "Forty-seven terabytes. That is the uncompressed size of 10 years of minute-level US equity OHLCV data for the S&P 500 universe — roughly 750,000 trading hours, each recorded at 1-minute intervals
--- The $40,000 Mistake One Quant Made at 2 AM The algorithm was elegant. Six months of backtesting had produced a Sharpe ratio of 2.1. The strategy targeted microstructural anomalies in after-hours t
--- The strategy returned 34.7% annualized in backtesting. Sharpe ratio: 1.82. Maximum drawdown: −6.2%. The equity curve climbed steadily for three years across 847 trades. Confident numbers. Clean eq
At 12:01 AM UTC on December 7, 2021, a single AWS Availability Zone in the us-east-1 region lost power. Within 90 seconds, a cascading failure had knocked out three of the five availability zones in t
A quant strategy that returns 34% annualized in simulation and −12% in live trading almost always has a data problem at its root. The gap is rarely a flawed alpha. It is missing data — gaps during tra
--- Every quantitative trader has been there. You run the backtest. The equity curve climbs smoothly to the right. Sharpe ratio of 1.8. Maximum drawdown of 4.2%. Win rate of 63%. You print the PDF, sh
--- "Show me the slope, not just the volume." A senior market maker once told me this during a late-night calibration session. She had just watched a junior analyst present a beautiful chart of order