TickDB API development tutorials, WebSocket integration and SDK examples
--- "Three hedge funds lost their prime brokerage relationships in Q3 2024. The cause was not a bad strategy. It was a data license." In January 2024, the SEC levied $35 million in fines against a sys
--- The Moment Your Strategy Breaks A systematic trader we'll call Chen ran his mean-reversion strategy on $50,000 for 18 months. Annualized returns hit 23%. Sharpe ratio: 1.42. Maximum drawdown: −7.3
--- The Problem That Breaks Every Backtest Consider this scenario: You pull 20 years of AAPL historical prices, run a simple moving average crossover strategy, and report a Sharpe ratio of 1.42. Confi
--- When you're building a cross-market trading system, the naive approach quickly becomes a maintenance nightmare: one WebSocket connection for US equities, another for Hong Kong stocks, a third for
--- "Your backtest returned 2.34 Sharpe. Your live system returned −0.8." The gap between historical simulation and live execution rarely comes from the alpha model. More often, it comes from data gov
A backtest that looks flawless in development can quietly bleed returns in production. Often, the culprit is not a flawed alpha signal. It is missing data — and how the system handles it. On any given
You have built a dashboard monitoring 100 US stocks with minute-level OHLCV data. It refreshes every 60 seconds. The engineering works perfectly. Then your finance team asks the question that stops ev
--- The Moment Everything Changes You spent eighteen months building your alpha model alone. Nights. Weekends. A spreadsheet that became a Python script that became a backtesting engine. You knew wher
--- The Data Gap That Breaks Your Backtest The strategy looks flawless on paper. Sharpe ratio of 1.42. Max drawdown under 8%. Win rate of 61%. You run it live for three weeks and watch it hemorrhage m
"Price is the effect. The order book is the cause." The question arrived at 11:47 PM on a Tuesday: could a single WebSocket connection handle 1,000 concurrent symbol subscriptions without the message
Every quant developer hits the same wall eventually. Your backtest runs fine. Your strategy logic is sound. Then you wire up live market data and watch your Python process fall behind by seconds — mis
--- "Price is the effect. The spread is the signal." On March 15, 2024, at 03:47 UTC, Bitcoin traded at $71,245 on Binance and $71,312 on Coinbase — a $67 spread that lasted exactly 340 milliseconds b
--- The Fundamental Problem A trade is the intersection of a buyer and a seller. One party initiated the transaction. One party crossed the spread. But raw exchange output does not label which side wa
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