Automated Forex Trading

5-Step Tutorial on How to Create and Test a Working Expert Advisor (EA) on MetaTrader-45-Step Tutorial on How to Create and Test a Working Expert Advisor (EA) on MetaTrader-4

MetaTrader 4 (MT4) features a built-in scripting language, MQL4, which enables the conversion of any trading idea into a fully automated Expert Advisor (EA). To accomplish this task, you don’t necessarily have to master MQL4 coding. You only need a software platform or another tool that can code on your behalf. 🔗 You can create MQL code by using a tool like EABuilder.

Basic Tips During the Process of Creating an Expert Advisor

The process of creating an EA begins with a clearly defined trading plan that includes logic, entry and exit rules, position sizing, and risk management. Important tips to keep in mind:

  1. Avoid hard-coding and add multiple input parameters for flexibility.
  2. Focus on proper order management. This means the EA must be able to correctly open, modify, and close trades while handling errors and execution delays.
  3. Backtesting any EA on MT4 is very important. Use precise symbols and timeframes, and assess not only profits, but also drawdowns and profit consistency.
  4. Never forget to test your Expert Advisor on a Demo Account, and later on a micro-lot account, before trading with serious money.
  5. When the time comes to trade with real money, make the right brokerage choice. You need an STP/ECN broker offering nothing less than fast execution and tight trading spreads. A free VPN can also be useful. 🔗 Compare ECN/STP Fx Brokers

 

StrategyQuant Review

StrategyQuant is a sophisticated platform that enables traders without programming skills to create, optimize, and backtest advanced automated trading strategies from scratch. It represents the next logical step in the evolution of automated trading.

 

StrategyQuant Review: The Evolution of Strategy Building

StrategyQuant users can create or find pre-made automated strategies to trade any financial market (Forex, Equities, Metals, Soft Commodities, etc.). The trader simply selects a market and timeframe, and StrategyQuant begins generating automated strategies. The trader can then choose the best-performing strategies, as well as test and optimize them against randomness.

□ StrategyQuant operates in four (4) modes: building, re-testing, improvement, and optimization

□ Building (from scratch) and optimizing automated trading systems for every financial market

□ Generating and testing thousands of random automated strategies (within hours)

□ Applying automated data-mining algorithms to generate EAs for MetaTrader4, TradeStation, and NinjaTrader platforms

□ The StrategyQuant 14-day trial version fully functional and it is not limited compared to the full package

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📊 Quantamental Trading Approach

Quantamental is a new trading approach that combines quantitative and fundamental analyses to forecast future market conditions. Trading and data mining are expected to converge in the near future.

 

In general, forecasting is the process of predicting future market conditions based on past and present data, as well as the analysis of key trends. All forecasting methods can be divided into two broad categories:

(i) Quantitative methods, based on mathematical models, and

(ii) Qualitative methods, based on educated guessing.

 

(1) Quantitative Forecasting

Quantitative forecasting involves predicting future data based on past data. It requires historical numerical data and assumes that recognizable patterns in the data series will continue into the future. This method is more effective for short- to mid-term decision-making.

There are two main types of quantitative methods:

(i) time-series methods, which use simple historical trends and patterns in the data series to generate forecasts;

(ii) explanatory methods, which use additional data as inputs into the forecasting model and attempt to combine two or more variables.

Key quantitative forecasting approaches in finance include:

(1) Average Forecasting Approach: Assumes that future market data will equal the statistical mean of past data.

(2) Naive Forecasting Approach: Assumes the time series follows certain seasonality. This approach is useful for financial time series with complex patterns that are hard to recognize and predict. Naive forecasting is often used as a benchmark for comparing other models.

(3) Drift Forecasting Approach: A more advanced version of the Naive Approach, incorporating an increase or decrease over time, called a drift.

 

 

(2) Fundamental Analysis

Fundamental analysis is a method of valuing a financial instrument by examining and evaluating all relevant internal and external factors. These factors include financial, economic, social, political, strategic, and other quantitative and qualitative variables. The goal of fundamental analysis is to determine a 'fair value' that can be compared with the current market price.

Fundamental analysis uses a wide range of real data—such as macroeconomic indicators, industry analysis, balance sheets, earnings reports, and key data releases—to assess the value of a financial instrument.

While fundamental and quantitative analyses differ in core ways, they also share many similarities. For example, when evaluating shares, both methods consider market capitalization, sector classification, price/earnings ratio, and dividend policy. As a result, quantitative models can be used to optimize the outcomes of fundamental analysis.

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StrategyQuant is a cutting-edge platform designed for building fully customized algorithmic trading strategies—tailored exclusively for professional traders seeking advanced automation and precision.

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