記事
Reimagining Classic Strategies (Part X): Can AI Power The MACD? MetaTrader 5のため
Join us as we empirically analyzed the MACD indicator, to test if applying AI to a strategy, including the indicator, would yield any improvements in our accuracy on forecasting the EURUSD. We simultaneously assessed if the indicator itself is easier to predict than price, as well as if the
Reimagining Classic Strategies (Part IX): Multiple Time Frame Analysis (II) MetaTrader 5のため
In today's discussion, we examine the strategy of multiple time-frame analysis to learn on which time frame our AI model performs best. Our analysis leads us to conclude that the Monthly and Hourly time-frames produce models with relatively low error rates on the EURUSD pair. We used this to our
Self Optimizing Expert Advisor With MQL5 And Python (Part V): Deep Markov Models MetaTrader 5のため
In this discussion, we will apply a simple Markov Chain on an RSI Indicator, to observe how price behaves after the indicator passes through key levels. We concluded that the strongest buy and sell signals on the NZDJPY pair are generated when the RSI is in the 11-20 range and 71-80 range
Gain An Edge Over Any Market (Part V): FRED EURUSD Alternative Data MetaTrader 5のため
In today’s discussion, we used alternative Daily data from the St. Louis Federal Reserve on the Broad US-Dollar Index and a collection of other macroeconomic indicators to predict the EURUSD future exchange rate. Unfortunately, while the data appears to have almost perfect correlation, we failed to
Multiple Symbol Analysis With Python And MQL5 (Part I): NASDAQ Integrated Circuit Makers MetaTrader 5のため
Join us as we discuss how you can use AI to optimize your position sizing and order quantities to maximize the returns of your portfolio. We will showcase how to algorithmically identify an optimal portfolio and tailor your portfolio to your returns expectations or risk tolerance levels. In this
Reimagining Classic Strategies in MQL5 (Part III): FTSE 100 Forecasting MetaTrader 5のため
In this series of articles, we will revisit well-known trading strategies to inquire, whether we can improve the strategies using AI. In today's article, we will explore the FTSE 100 and attempt to forecast the index using a portion of the individual stocks that make up the index
Gain An Edge Over Any Market (Part IV): CBOE Euro And Gold Volatility Indexes MetaTrader 5のため
We will analyze alternative data curated by the Chicago Board Of Options Exchange (CBOE) to improve the accuracy of our deep neural networks when forecasting the XAUEUR symbol
Self Optimizing Expert Advisor With MQL5 And Python (Part IV): Stacking Models MetaTrader 5のため
Today, we will demonstrate how you can build AI-powered trading applications capable of learning from their own mistakes. We will demonstrate a technique known as stacking, whereby we use 2 models to make 1 prediction. The first model is typically a weaker learner, and the second model is typically
Self Optimizing Expert Advisor with MQL5 And Python (Part III): Cracking The Boom 1000 Algorithm MetaTrader 5のため
In this series of articles, we discuss how we can build Expert Advisors capable of autonomously adjusting themselves to dynamic market conditions. In today's article, we will attempt to tune a deep neural network to Deriv's synthetic markets
Reimagining Classic Strategies in MQL5 (Part II): FTSE100 and UK Gilts MetaTrader 5のため
In this series of articles, we explore popular trading strategies and try to improve them using AI. In today's article, we revisit the classical trading strategy built on the relationship between the stock market and the bond market