Forex Options Quotes

Track Your Favorite Instruments I have met so many traders whose trading journey ends up looking something like this: One month they’re trading stocks (“we’re in a bull market, man!”) When that didn’t work out, they jump to options (“strangles and spreads, that’s where it’s at, baby!”) When that didn’t work out, they jump to forex (“I just bought this forex robot, it does all the trading for me!”) When that inevitably didn’t work out, they jump onto Bitcoin (“it’s gonna replace money!”) When that didn’t work out, they jump on social media (“I saw this ad on Facebook about trading binary options…”) By now, they have to go back to work, because they have blown up whatever trading capital they had left.

Find a price curve anomaly. Decide for a market inefficiency to exploit – or discover a new one. The best known inefficiencies are listed in the next chapter. Think about which price curve anomaly this effect could produce (an anomaly is any systematic deviation from randomness). Describe it with a quantitative formula or at least a qualitative criteria. You’ll need that for the next step.

As a result, private traders have a slightly higher annual loss rate of about 65%, but certainly not 95%, not even beginners.

When analyzing robot strategies, one can notice such a martingale system from telltale peaks in the lot size. For this reason, robots or signal providers often increase not the number of lots, but the number of trades, which is less suspicious.

You can now create the user interface of your trading system. Determine which parameters you want to change in real time, and which ones only at start of the system.

such as the Cold Blood Index described on the Financial Hacker blog.

Alice: "Before the comparison with the threshold, I apply a Fisher transformation. This gives the curve a Gaussian distribution with sharp and well defined oscillations, so we get less false signals." Bob: "I have no idea what you're talking about.

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In Trading, Every Setback is a Setup for a Comeback.

An advise call generates trade rules in C code; Detrend = SHUFFLE tests a strategy with a random price curve; NumTotalCycles can generate statistics from multiple simulation runs; plotHistogram plots histograms.

But options are often bought as an insurance against unwanted price moves of the underlying.

Depending on length and resolution it can be a couple hundred dollars. Options data selling is apparently even more lucrative than options selling.

The reality check described here can determine that a strategy won't work. But the opposite is not true: it cannot with the same certainty determine that it will work.

An option is not a financial asset. It is a simple contract that gives its owner the right to buy (call option) or sell (put option) a certain amount (the multiplier, usually 100) of a stock, a future, or some other asset (the underlying) at a fixed price (the strike) at or before a fixed date (the expiry). Better read the previous sentence twice.

American style options can be exercised anytime, European style options only at the expiry date.

Therefore, in stead of candle patterns any other set of signals or indicators could also be used for the advise function and compared with the PATTERN method, or with other machine learning functions like decision trees, perceptrons, or neural networks from the R statistics package.

The list of signals is passed to the function as a float array named sig. The float type is a variable type similar to var, but has lower precision and thus consumes less memory. sig[0] is the first signal passed to the advise function - in this case, it's priceHigh(2). sig[1] is the second signal - priceLow(2) - and so on. The signals are used inside the function just like the elements of a series.

AdviseLong and adviseShort are general functions for machine learning. Their parameters are the learning method, the training target, and the signals from which this target is to be trained.

The adviseShort call here has no parameters. This tells Zorro to simply use the methods and signals from the last advise call, which was the preceding adviseLong call. This saves some paperwork in the script.

It is also usual to trade not only single options, but combinations (combos) of different options types. Thus there are innumerable possibilities for options strategies.

If the underlying price (the spot price) of a call option lies above the strike, the option is in the money; otherwise it’s out of the money.

For instance, static variables keep their value between function calls;

Due to the premium, options can still produce a profit to their seller even if the underlying moves a bit in the wrong direction. That's the seller advantage.

It is a "known fact" that 95% of all private traders lose all their money in the first 12 months.

It is a "known fact" that 95% of all private traders lose all their money in the first 12 months. Not true - at least not with completely random trading and one trade per day. You can see from the profit distribution that only about 55% lose money at all (the sum of the red bars with negative profit), while 45% end their trading period with a profit. Of course, they won't attribute their success to the bell curve, but to their trading skills.

The profit distribution of real traders is not a Gaussian, but a Lévy distribution. It has a smaller peak and fatter tails. That means the losers lose more, and the winners take more than in a random-trading situation.

But we hope a merciful margin call saved them early.

This effect is called Fooled by Randomness.

and even Saturn can not impress the S & P500 index.

adviseLong function stores a "snapshot" of the signal parameters in an internal list

Sure, WFO allows to use different price curves for training and for testing. However, you will usually choose your strategy just because of their positive WFO test. This means that the 'out-of-sample' price data in the test period are not so out of sample anymore, since they were used for selecting the best strategy.

OptimalF is the already mentioned optimal investment factor that Zorro has calculated from the balance curve of the backtest, using the Ralph Vince algorithm.

the square root method always keeps the same distance from the account balance to the maximum expected drawdown.

If your capital has doubled, you can only pay out about 60% of the profit. The remaining 40% must remain on the account as a buffer to compensate future drawdowns.

Strategy parameters must be trained individually for any asset. It is rare that multiple assets share the same parameters.

With high leverage systems, increase your investment only proportionally to the square root of the capital growth.

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The best indicator is whether or not a green (red) candlestick following a red (green) candlestick which engulfs the candlestick to the left is the best indicator of a coming reversal.

Assemble all your metal strength and go back to step 1.

Instead of exiting after a certain time, raise the stop loss by a certain amount per hour. This has the same effect, but will close unprofitable trades sooner and profitable trades later.

Variants that maintain most anomalies are oversampling, detrending, or inverting the price curve.

If the system stays profitable with those variants, but not with randomized prices, you might really have found a solid system.

Dependent on whether you trade a single asset and algorithm or a portfolio of both, you can calculate the optimal investment with several methods. There’s the OptimalF formula by Ralph Vince, the Kelly formula by Ed Thorp, or mean/variance optimization by Harry Markowitz.

Important: Do not reinvest the profits during the test runs. Do that only at the end when your strategy is complete. Until then, test with fixed lot sizes onyl. Reinvesting can distort the equity curve completely and can affect the result to a point that it useless.

The filter is at least as important as the trade signal, if not more – but it’s often forgotten in trade systems.

The in-sample periods are used for training, the out-of-sample periods for testing.

A market inefficiency has normally only a very weak effect on the price curve. So your algorithm must be really good in distinguishing it from random noise.