TickDB API development tutorials, WebSocket integration and SDK examples
A production trading system at 2 AM. The order book for 150 symbols needs updating every 500 milliseconds. The single WebSocket connection that worked flawlessly during development is now dropping mes
On August 31, 2020, Apple executed a 4-for-1 stock split. Anyone running a mean-reversion strategy on AAPL's 5-year historical dataset without adjusting for that split would have encountered a catastr
--- The Moment You Outgrow the Standard API There is a point in every trading infrastructure journey where the standard API stops being enough. It happens quietly. Your team has been using TickDB for
The code worked beautifully on your laptop. Then your teammate deployed it to the server, and the backtest returned nothing but zeros. The problem wasn't the algorithm. It was the infrastructure. For
--- "Price is the effect. The order book is the cause." That principle guides every quant researcher who spends hours perfecting a strategy — only to watch it degrade because the underlying data pipel
--- "Every market data platform has a ceiling. The question is whether that ceiling matches your strategy." That line is from a conversation with a quant researcher who spent three weeks building a ti
--- The $10-per-Month Trading System That Actually Works The hardest part of quantitative trading isn't finding alpha. It's surviving the infrastructure. I know this because I've been that developer —
"Three hours. That's how long it took to find the bug." The on-call engineer stared at a wall of text in their terminal. Somewhere in a 50,000-line log file from last night's production incident was t
A data vendor says "unlimited subscriptions." You subscribe to 100 symbols. It works. You subscribe to 500. Still works. You subscribe to 1,000. Then your dashboard freezes. Your Python process consum
The dream is simple: start algorithmic trading without spending a cent. The reality is more nuanced. Every free quant stack encounters the same three walls — data quality that breaks backtests, framew
--- The Moment Everything Breaks At 09:30:00.123 ET on a typical trading day, the floodgates open. Your WebSocket connection receives 847 market data updates in the first 2.3 seconds after the market
At 3:47 AM on a Tuesday, a quant trader I'll call Marcus hit a wall. He had spent six weeks building a microstructure alpha model — order flow imbalance detection, queue position estimation, the works
The door opened at 9:47 AM on a Tuesday in March. The hiring manager at a prominent systematic fund glanced at the candidate's laptop and asked, "Show me your backtest framework." What followed — the
The moment you try to subscribe to 150 symbols simultaneously over a single WebSocket connection, the system starts lying to you. Not maliciously — but consistently. Heartbeat messages get delayed bec
--- Price is the effect. The underlying model is the cause. Two quantitative trading teams run similar mean-reversion strategies on US equities. Team A pays $2,400 per month on a flat-rate plan but le
--- Price is the effect. The order book is the cause. That phrase hung on my monitor for three weeks when I started building my first quant strategy. I had spent eight years as a software engineer, wr
--- The Audit That Changed Everything The quant team had been running the same mean-reversion strategy for eighteen months. Sharpe ratio: 1.42. Max drawdown: −6.3%. Everything looked clean on paper. T
--- "I set up my strategy at 11 PM on a Sunday. By Monday morning, it had stopped collecting data at 3:47 AM because the process crashed silently. I lost the entire Asian session." This is the most co
The problem with correlation matrices is that they tell you what happened, not what might happen next. A Pearson correlation of 0.82 between NVIDIA and Taiwan Semiconductor tells you these two assets
--- A strategy that returns 23% in backtesting and 8% in live trading is not a bad strategy. It is a strategy with a slippage problem. For three months, the quantitative team at a mid-size hedge fund