The CSI indicator was first introduced Welles Wilder in the book called “New Concepts in Technical Trading Systems”. CSI combines 4 factors, forex margin used calculation determine the best commodities for trading.
A high CSI rating demonstrates that the commodity has strong volatility characteristics and is trending. Such commodities with high CSI rating are very volatile, and have the potential to make the fastest profits in the shortest period of time. Although high CSI values imply trending markets characteristics, the indicator is designed for short-term traders who can handle the risks associated with highly volatile markets. It would be great if you post the code hear for downloading.
Sorry, don’t have it for MT4 yet. In this article we’ll look into a real options trading strategy, like the strategies that we code for clients. This one however is based on a system from a trading book. The system that we’ll examine here is indeed able to produce profits. It does not require close monitoring. All statements with which I, of course, highly sympathize. Sell a 6 weeks call and a 6 weeks put of an index ETF.
If the underlying price touches one of our strike prices, thus threatening an in-the-money expiration, buy back that option and immediately sell a new option of the same type, but to a further expiration date, and a premium that covers the loss. Wait until all options are expired, then go back to 1. If you have a bit experience with options, you’ll notice that rule 1 describes a strangle combo. And you’ll next notice something strange with rule 2.
Right, such a system can never lose, since any loss would apparently be compensated by the premium from the new trade. Have we finally found the Holy Grail, an ever-winning system? 10 in any direction until expiration. Otherwise the loss can quickly reach the thousand dollar zone. But wait, we have rule 2, which will certainly save the day! Let’s put that to the backtest. The run function sets up the backtest time and other parameters for the backtest as well as for live trading.
It’s a daily script, and the function runs every day at 3:20 pm Eastern Time. It uses two historical data files for the backtest. The next part of the code implements the miraculous rule 2. This way we’re punishing the market for going against us. The printf function just stores that event in the log, so that we can go through it and better see the fate of those trades. The last part of the code is the strangle.
Margin affects the required capital and thus the backtest performance, so it should reflect your broker’s margin requirement. By default, the margin of a sold option is the premium plus some fixed percentage of the underlying that’s set up in the asset list. But brokers often apply a more complex margin formula for option combos. The backtest from 2011-2016 needs only about 2 seconds.
935 drawdown when we always compensate our loss with a new trade? Let’s try the same strategy without the rule 2. Of course at cost of higher risk, since no limiting mechanism is in place. We could now test other option combos instead of the strangle, for instance a condor for limiting the risk. We can run an optimization for finding out how the profit is affected by different premiums and expirations. I leave that to the reader.
Rolling over with loss compensation establishes in fact a Martingale system. And such a system fares no better in option trading than in the casino. In the casino you have at least the same chance with every play. Artificial options data Since the system does not rely on goblethegooks, we can check whether the artificial options data that we created in the first part of this mini series can be used for testing this system. The backtest results above were with real options data. Artificial data represents a more efficient market situation, since its option premiums are identical to their theoretical values, and fundamentals such as earnings reports play no role.