Trading is a stochastic process; i.e, the outcome of each trade is not deterministic and has a distinctly random bias. Sometimes you win, sometimes you lose. Sometimes you win big, sometimes you take it in the pants. Often your winning or losing trades will cluster together in a row; Other times, they’ll alternate in a more noisy fashion.
This poor guy opened a large futures position into the weekend which gapped down like 650 points against him when it reopened, taking with it nearly his entire account. Ouch!
A BBC reality program (ala Wall Street Warriors, only more thoughtful/cerebral) where a hedge fund manager takes a group of everyday people (from a housewife to a computer programmer) and gives them money to trade at his offices in London. Over time he fires the unsuccessful traders and gives the profitable ones more $$$ to trade. The top performing trader at end will surprise you.
When I first started trading nearly 100% of my focus was on the percentage of my trades that were winners (win probability). After all, winning feels good and I wanted to maximize that much as possible. But over time I learned that the win probability of a system is largely irrelevant (regardless of how good it makes you feel); Instead of worrying so much about winning I really should have been focusing on my system’s expectation.
The S&P futures trading pit audio feed during the May 2010 Flash Crash.
“According to reports from some who saw MF Global’s trading records and balance sheet before the company filed for bankruptcy on October 31, the firm’s books had incomplete transactions, and numbers that just didn’t seem to add up.”
(Click the post title for the full story)
A nice collection of white papers on Algorithmic Trading (click on post title).
Interesting paper on detecting Black Swans (click post title for a PDF). Here’s the abstract:
Financial markets are well known for their dramatic dynamics and consequences that affect much of the world’s population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framework to understand and diagnose financial bubbles from rational expectations and was recently extended to negative bubbles and rebounds. Using the JLS model, we develop an alarm index based on an advanced pattern recognition method with the aim of detecting bubbles and performing forecasts of market crashes and rebounds. Testing our methodology on 10 major global equity markets, we show quantitatively that our developed alarm performs much better than chance in forecasting market crashes and rebounds. We use the derived signal to develop elementary trading strategies that produce statistically better performances than a simple buy and hold strategy.
Seasoned Dapper Willy-Loman-Aspiring $400,000/year Wall Street Stiff Flying First Class Thinking He’s Comfortable!