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9+ years
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60
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11
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1
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And I have done a lot of academic research on financial markets.
From 2012, I work as a Quant.
Forex, stock and futures are my main trading varieties.
I can use MQL4, MQL5, C++, MySql, and Python.
1. What is this Rising volatility and falling volatility are not the same, whether it is academic research or actual testing has shown this point. The original ATR indicator is calculated by putting up and down fluctuations together. This indicator is to calculate separately the upward volatility and the downward volatility, which can better help you study the market. 2. Indicator description There are two
1. What is this. This is a very rigorous indicator to show different market trading sessions. It shows the main markets: NewYork, London, Frankfurt, Sydney, Wellington, Tokyo. Very important: Different markets have different start and end dates for daylight saving time, and the trading session of a market can vary depending on daylight saving time and winter time. Also, the daylight saving time system is different for
1. What is this. This is a very rigorous indicator to show different market trading sessions. It shows the main markets: NewYork, London, Frankfurt, Sydney, Wellington, Tokyo. Very important: Different markets have different start and end dates for daylight saving time, and the trading session of a market can vary depending on daylight saving time and winter time. Also, the daylight saving time system is
1. What is it The classic Bollinger Bands and the Bollinger Bands indicator built into the system, they have the same mean period and deviation period. And the method of average is just the simple moving average method. The deviation method used is just the standard deviation method. All this limits our research because: Sometimes we would like to have longer period averages + shorter period deviations. Sometimes we want moving averages that are not limited to simple
This article describes the construction of the custom optimization criterion R-squared. This criterion can be used to estimate the quality of a strategy's balance curve and to select the most smoothly growing and stable strategies. The work discusses the principles of its construction and statistical methods used in estimation of properties and quality of this metric.
Before launching a robot on a trading account, we usually test and optimize it on quotes history. However, a reasonable question arises: how can past results help us in the future? The article describes applying the Monte Carlo method to construct custom criteria for trading strategy optimization. In addition, the EA stability criteria are considered.
In this article, I will tell you how to successfully trade by merging a very well-known strategy and a neural network. It will be about the Thomas DeMark's Sequential strategy with the use of an artificial intelligence system. Only the first part of the strategy will be applied, using the Setup and Intersection signals.
This article suggests a variant of an adaptive system that consists of many strategies, each of which performs its own "virtual" trade operations. Real trading is performed in accordance with the signals of a most profitable strategy at the moment. Thanks to using of the object-oriented approach, classes for working with data and trade classes of the Standard library, the architecture of the system appeared to be simple and scalable; now you can easily create and analyze the adaptive systems that include hundreds of trade strategies.
