ZeroMQ – How To Interface Python/R with MetaTrader 4

27 August 2017

In this post, we present a technique employing ZeroMQ (an Open Source, Asynchronous Messaging Library and Concurrency Framework) for building a basic – but easily extensible – high performance bridge between external (non-MQL) programming languages and MetaTrader 4.   Reasons for writing this post: Lack of comprehensive, publicly available literature about this topic on the […]

LVQ and Machine Learning for Algorithmic Traders – Part 3

17 June 2017

In the last two posts, LVQ and Machine Learning for Algorithmic Traders – Part 1, and LVQ and Machine Learning for Algorithmic Traders – Part 2, we demonstrated how to use: Linear Vector Quantization Correlation testing ..to determine the relevance/importance of and correlation between strategy parameters respectively. Yet another technique we can use to estimate […]

LVQ and Machine Learning for Algorithmic Traders – Part 2

14 June 2017

    In LVQ and Machine Learning for Algorithmic Traders – Part 1, we discussed and demonstrated a technique (Linear Vector Quantization) to decipher the relevance and relative importance of each feature variable in the dataset under study. In doing so, algorithmic traders would be able to isolate which of a dataset’s features (read: strategy […]

LVQ and Machine Learning for Algorithmic Traders – Part 1

8 June 2017

Algorithmic traders across all spectra of asset classes, often face a rather daunting challenge. What are the best inputs for an algorithmic trading strategy’s parameter space? Different algorithmic trading strategies (whether manual or automated) will each have their own unique set of parameters that govern their behaviour. Granted.. Genetic and Walk-Forward Optimization will help algorithmic […]

Quantitative Modeling for Algorithmic Traders – Primer

3 May 2017

Quantitative Modeling techniques enable traders to mathematically identify, what makes data “tick” – no pun intended 🙂 . They rely heavily on the following core attributes of any sample data under study: Expectation – The mean or average value of the sample Variance – The observed spread of the sample Standard Deviation – The observed […]

Hidden Markov Models & Regime Change: DARWINs vs. S&P500

24 April 2017

In this post, we will employ a statistical time series approach using Hidden Markov Models (HMM), to firstly obtain visual evidence of regime change in the S&P500. We will then compare the index’ performance to a DARWIN Portfolio, between June 2014 and March 2017. Detecting significant, unforeseen changes in underlying market conditions (termed “market regimes“) […]

差价合约是相当复杂的资产,并且由于杠杆率会带来极高的风险和损失。 69 %的零售投资者账户在与此提供商交易差价合约时会损失资金。 您应该考虑是否了解差价合约的工作原理,以及您是否有能力承担损失资金的高风险。