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Trading: A Dream or the Path to Financial Independence?

It is no secret that times are tough. V.A.T has reached 20%. The fuel duty increase…

Assess Your Trading Risk With Monte Carlo Analysis

Before trading the markets with real money, it’s essential to understand the risk of the trading strategy or method you intend to use. One way to assess risk is by testing your strategy over historical market data to see how well the strategy would have done in the past. While this so-called backtesting approach is very helpful, one of the drawbacks is that the future is never exactly the same as the past. Employing Monte Carlo analysis can help to address this problem.

Increase Trading Profits by Exploiting Dependency

Trade dependency is the characteristic in which one trade depends on the previous trade. For example, in some trading systems or methods, winning trades tend to follow other winning trades, and losses tend to follow losses. This is known as positive dependency: wins follow wins and losses follow losses. In negative dependency, wins tend to follow losses, and losses tend to follow wins. Properly exploiting dependency can increase profits and reduce risk.

Base Your Trade Size on the Risk

It’s a common axiom of investing that the greater the risk the greater the reward. One method of sizing short-term trades based on this principle is fixed fractional position sizing. The idea behind the fixed fractional method is that you base the number of contracts or shares on the risk of the trade. Fixed fractional position sizing is also known as fixed risk position sizing because it risks the same percentage or fraction of account equity on each trade.

Detecting Over-Fit Trading Strategies

One of the risks of systematic or algorithm trading is that the trading system may be over-fit to the market. Over-fitting means that a strategy that has been designed to work on a given set of market data doesn’t generalize well to other data. Such a system may look good in historical testing but will trade poorly in real time. This article presents a statistical technique to detect over-fit strategies.

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