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Option trading is fantastic, when you know what you are doing - it is dangerous if you don't.

Why?

Multiple variables and instances define the price of an option and you can apply various option strategies to trade at specific price constellations of an underlying.

The referring trading strategy was a credit spread with weekly options and after eight days (the time used in the example), you keep the premium made. A high probability trade setup was found by some moving average crossings and underlined by the trading strategy, where you will be profitable if the share price goes up, slightly retraces or moves sideways. The trade had the following components:

• 10 Bull Put Spreads: You expect the price of the underlying to stay above a defined minimum price level to keep the premium received
• Every contract controlled 100 shares
• The spread of the option strike prices was \$2.50

In the shown example, the trade was made and the premium kept. It all sounded good; however, to evaluate this trading example, in respect to repeatability, let us consider the following:

• \$30 average commission to open the trade and in case you have to close it, you again pay \$30
• The risk of the trade (Spread x Contracts): \$2.50 x 10 x 100 = \$2,500
• Potential net-return of the trade (Premium - Commission): \$230 - \$30 = \$200

For every trade, an odds approximation is essential: In this example, let us

take 10 trades and out of those nine winners and one loser: What will be your trade balance?

• The expected net return of \$200, times nine positive trades = \$1,800 (gain)
• If you get caught once with a max loss = \$2,500 + \$30 = \$2,530 (loss)

In essence, the potential risk of the trade is not in balance with the potential reward.

To quantify the real probability of this trading strategy and what it probably will do to your trading account: Take the at-the-money Straddle plus Strangle premium, divide it by two, add and subtract this result to and from the actual stock price. This will give you the 1-Sigma price range, where the market maker expects the price of the underlying to end up being at expiration. When using a Gaussian distribution the 1-Sigma range contains the final price of the underlying at expiration with a 68% probability.

Rounding the 1-Sigma range to 70% and relating seven winning trades and three losers, your trade balance after 10 trades might end up being: \$1,400 - 3 x \$2,530 = -\$6,190 (loss).