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trend_without_hiccups_-_a_kalman_filter_approach.pdf
trend_without_hiccups_-_a_kalman_filter_approach.pdf
Do we have this Kalman filter for mt4?
Do we have this Kalman filter for mt4?
sebastianK
Check these :
https://www.mql5.com/en/forum/172923/page18
https://www.mql5.com/en/forum/173235/page89
https://www.mql5.com/en/forum/173235/page88
https://www.mql5.com/en/forum/183054/page4
sebastianK
Check these :
https://www.mql5.com/en/forum/172923/page18
https://www.mql5.com/en/forum/173235/page89
https://www.mql5.com/en/forum/173235/page88
https://www.mql5.com/en/forum/183054/page4
Thank you![](https://c.mql5.com/forextsd/smiles/smile.png)
There are lot of Forex ebooks available at internet and we can learn Forex through these books. But books should be good and always very detailed. When i need some Forex related books then i generally download them from here: http://forexbrokersaz.com/uk/books/
Here are the two books which are not downloadable from the link in post #1228:
Kathy Lien: The Little Book of Currency Trading: How to Make Big Profits in the World of Forex
https://www.sendspace.com/file/v6p0kj
Sam Weinstein: Secrets for Profiting in Bull and Bear Markets
https://www.sendspace.com/file/8qmqyq
Sixer
detecting_fake_price_movements_-_a_convergence_divergence_indicator.pdf
Debates & Discussions
The study analyses quantitative models for financial markets by starting from geometric Brown process and Wiener process by analyzing Ito’s lemma and first passage model. Furthermore, it is analyzed the prices of the options, Vanilla & Exotic, by using the expected value and numerical model with geometric applications. From contingent claim approach ALM strategies are also analyzed so to get the effective duration measure of liabilities by assuming that clients buy options for protection and liquidity by assuming defaults protection barrier as well. Furthermore, the study analyses interest rate models by showing that the yields curve is given by the average of the expected short rates & variation of GDP with the liquidity risk, but in the case we have crisis it is possible to have risk premium as well, the study is based on simulated modelisation by using the drift condition in combination with the inflation models as expectation of the markets. Moreover, the CIR process is considered as well by getting with modification of the diffusion process the same result of the simulated modelisation but we have to consider that the CIR process is considered in the simulated environment as well. The credit risk model is considered as well in intensity model & structural model by getting the liquidity and risk premium and the PD probability from the Rating Matrix as well by using the diagonal. Furthermore, the systemic risk is considered as well by using a deco relation concept by copula approaches. Moreover, along the equilibrium condition between financial markets is achieved the equity pricing with implications for the portfolio construction in simulated environment with Bayesian applications for Smart Beta. Finally, Value at Risk is also analyzed both static and dynamic with implications for the percentile of daily return and the tails risks by using a simulated approach.Debates & Discussions
This paper will provide more information of complexity science and how it can be used in finance. The following topics are covered in this paper: (a) complexity science, (b) agents, (c) automata, (d) the optimization problem, and (e) Strategy filtering. Within the optimization problem a section on trading strategies and simulation methods will be put forth. In the strategy filtering section a review of agent based models will be discussed to pick a maximizing solution. The goal of this paper is to introduce complexity science and system thinking to financial trading and economic problems.Debates & Discussions
In a recent empirical study by Glabadanidis ("Market Timing With Moving Averages" (2015), International Review of Finance, Volume 15, Number 13, Pages 387-425; the paper is also available on the SSRN and has been downloaded more than 7,500 times) the author reports striking evidence of extraordinary good performance of the moving average trading strategy. In this paper we demonstrate that "too good to be true" reported performance of the moving average strategy is due to simulating the trading with look-ahead bias. We perform the simulations without look-ahead bias and report the true performance of the moving average strategy. We find that at best the performance of the moving average strategy is only marginally better than that of the corresponding buy-and-hold strategy. In statistical terms, the performance of the moving average strategy is indistinguishable from the performance of the buy-and-hold strategy. This paper is supplied with R code that allows every interested reader to reproduce the reported results.