Do you know if it's possible to do this ?

- Develop a Dll to bridge MTQ4 and Matlab, by opening a non-volatile named objects inside an indicator for interacting w/ Matlab through it.

Which kind of non-volatile named objects inside an indicator I could use in this case ?

regards

]]>Due to the confidentiality of this blog (it's a private one and you can watch it only on my personnal invitations) , there are some rules :

If you or anyone else in the group wants to publish a paper in any form about those ideas from this blog, we want the name of the ideas'owner in the paper (if it is a formal paper) or acknowledgement for those ideas. Why ? Because a member named Oscar has brillant ideas to share here, concepts which are more than extension or consequences of the infinite matrix concept, **This is a new theory**.

Basic concepts on which Oscar'stheory is built on :

- The forex is an infinite matrix. (jaguar1637)

- Objects from this infinite matrix can be seen as valid, if determinant or vectors can be extracted from those objects. (jaguar1637). Infinite matrix is an extension of the GUT theory. In example, a candle can be seen as a squared matrix belonging to the infinite matrix created by the forex. So, any data values from those candles or objects w/ determinant root are valids

- this implies the nature of Vectors found here, are valid and reliable as data values (jaguar1637)

**Consequences :**

**T****he Oscar ' theoriy is based on vectors.** Those vectors are obviously understandable and reliable. The vectors calculation provides impressive good results (outside the concept of determnated/non determinated market or concept of chaotic / predictable market)

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Stochastic volatility models are a category of stochastic processes that have stochastic (random) second moments. Stated another way, they have random volatility or are conditionally heteroskedastic. "Stochastic volatility model" is a technical term. While all stochastic volatility models have stochastic second moments, not all models that have stochastic second moments are called stochastic volatility models. In finance, two categories of stochastic processes are widely used to model stochastic second moments. One is stochastic volatility models. The other is ARCH/GARCH models.

Both ARCH/GARCH and stochastic volatility models derive their randomness from white noise processes. The difference is that an ARCH/GARCH process depends on just one white noise W. That white noise directly determines innovations in the ARCH/GARCH process while also indirectly determining innovations in its second moments. Stochastic volatility models generally depend on two white noises, V and W. One directly determines innovations in the stochastic process. The other directly determines innovations in its second moments.

Stochastic volatility models come in forms far more diverse than those of ARCH or GARCH models

I heard With athe Monte Carlo algoritm, it's possible to build a function and obtain a time-serie as result. Now, the idea is to reverse the processing.

From a time-series, us the Monte Carlo to retrieve the primary and main function and guess the forecast value

This approcach based on an EA, is shown here : http://forum.mql4.com/37182

**Something has been made in : http://mcfx.collective2.com/**

This arbitrage strategy uses Monte Carlo statistical sampling methods derived from those commonly used to approximate a solution to the transport equation in nuclear systems.

This EA produces Monte Carlo simulations of the Forex market every minute for a number of periods into the future. A mean and standard deviation are obtained from this projection, and are fed into a trade decision subroutine. This information is then used to enter the market when conditions are favorable and exit when they are not.

This system centers around a very robust risk management strategy using the Monte Carlo method. Instead of seeking a high win rate by having an SL thousands of pips away (which can result in margin calls and losses to subscribers), we use sound statistical methodology. Take-Profit (TP) and Stop-Loss (SL) levels are set dynamically based on the projected means and standard deviation upon execution. Stop-Loss levels are tight, sometimes set as low as 15 pips away from the entry, so while we may not have an artificially high win rate, what you see in our equity curve is representative of the drawdown percentage you should expect in your account. It is very important to scale your account properly, however. Please see the discussion below on risk management.

This instance of the MCFX system uses multiple positions per traded pair executed within a probability cloud. Because of the multiple position strategy, this system is intended to be used in larger accounts.

The TP and SL are set using Monte Carlo standard deviation data when an entry is executed. The lot size is calculated such that 3% of the account will be lost if the SL is hit on all possible positions at any given time. This risk is divided by however many instruments are being traded and the maximum number of open positions in each instrument. For example, if we're trading the USDCAD and GBPUSD and a maximum of 30 positions in each, every position opened risks 0.05% of the principal in the account, such that if the maximum number of positions are entered in both pairs simultaneously, 3% of the account will be at risk. Note that even though this is a conservative and bounding scenario, it is possible to have a string of losing transactions causing the user to lose more than 3% of their account within any given period.

Please note that the risk percentage is approximate since C2 does not allow fractional lots. To allow for the most granularity in position sizing, the maximum account size was selected for broadcast.

If you have a $1,000,000 account and would like to trade with a 3% risk, your scaling factor will be 1. Otherwise, please calculate your scaling factor as follow:

sf = AB / rAB * R / rR

where:

sf = scaling factor

AB = account balance (yours)

rAB = reference account balance (mine)

R = desired risk (yours)

rR = reference risk (3%)

For example, a $10,000 account at 6% risk would use a scaling factor of 0.02 (10k/1M * 6/3)

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Developed by Marc Chaikin, Chaikin Money Flow measures the amount of Money Flow Volume over a specific period. Money Flow Volume forms the basis for the Accumulation. Look for sharp increases in volatility prior to market tops and bottoms, followed by low volatility as the market loses interest.

Chaikin Money Flow is a technical indicator used to determine

if a security is under accumulation or distribution

Let’s assume that a rather bullish security has quite a high closing price still within the day’s range and a rising volume and the signal is founded on this.

If a security is now shut with a low closing price inside its day’s range with a high volume this means the stock is weak.

There is pressure to sell once a security has closed during the bottom half of the trading range of a time frame and buying pressure once a security shuts in the top half of the range.

The exact number of the tool’s time frame may be different according to the sensitivity and time constraints of investors.

The very first bear signs come with a Chaikin Money Flow below zero. A stock will be under distribution conditions or remains under pressure to sell if the point is below zero.

The amount of time the Chaikin Money Flow has been below zero is another potential bearish sign. Negative for longer, the higher the possibility for greater pressure to sell or distribution. If the stock stays under zero for a long time then greater downward pressure on the price may emerge as the stock attracts a greater bearish mentality.

The amount of selling pressure is another potential bearish sign.

Generally points at either side of the zero line (below or above 0.10) are too weak to declare a bull or bear position.

Move beyond +0.10 and a bull scenario occurs. Equally, below -0.10 and it becomes a bear state.

Taking this further, above +0.25 will result in strong pressure to buy and below -0.25 will create strong pressure to sell.

**The Bottom Line**

Chaikin Money Flow’s best quality is that it frequently provides early sights of impending reverses in price. It’s best to confirm this by combining other indicators but as Chaikin Money Flow is an advanced tool, this should provide an advantage over others as you are able to confirm the change of control and deal before them.

But, as with other tools, Chaikin Money Flow can give incorrect signs hence the need for extra independent verification. As it uses a moving average it is frequently a lagging tool so is best used verifying other signs. But as far as indicating early reverses for short term trends it’s one of the best indicators.

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