Forex trading robots, also known as expert advisors (EAs), are automated trading tools that can execute trades on behalf of the trader. These robots are designed to identify trading opportunities and execute trades based on preset rules and parameters.
One popular trading strategy used by forex robots is the moving average crossover. In this strategy, the robot uses two moving averages, one short-term and one long-term, to identify buy and sell signals. The short-term moving average crosses above the long-term moving average to generate a buy signal, while a cross below generates a sell signal.
In this article, we will explain step-by-step how to build a forex robot using the moving average crossover strategy, without relying on any external libraries or APIs.
Step 1: Choose a programming language
The first step in building a forex robot is to choose a programming language. There are several programming languages that can be used to build forex robots, including Python, Java, C++, and MQL4/MQL5.
For this tutorial, we will be using Python, as it is an easy-to-learn, versatile language with a large community and many libraries available for data analysis and machine learning.
Step 2: Get historical data
To build a forex robot, we need historical data to test our strategy. There are several sources of historical forex data available online, including free and paid sources.
We will be using the free historical data available on the Dukascopy website. The data is available for download in CSV format and covers several currency pairs from 2003 to present.
Step 3: Load the data
Once we have the historical data, we need to load it into our Python environment. We will be using the Pandas library to load and manipulate the data.
To load the data, we can use the following code:
“`python
import pandas as pd
data = pd.read_csv(‘EURUSD.csv’)
“`
Step 4: Calculate moving averages
Next, we need to calculate the moving averages for our strategy. We will be using the pandas rolling function to calculate the moving averages.
“`python
short_ma = data[‘Close’].rolling(window=20).mean()
long_ma = data[‘Close’].rolling(window=50).mean()
“`
In this example, we are using a short-term moving average of 20 periods and a long-term moving average of 50 periods.
Step 5: Generate signals
Once we have calculated the moving averages, we can generate buy and sell signals based on the crossover of the two moving averages.
“`python
data[‘Signal’] = 0
data[‘Signal’][20:] = np.where(short_ma[20:] > long_ma[20:], 1, 0)
data[‘Position’] = data[‘Signal’].diff()
“`
In this example, we are generating buy signals when the short-term moving average crosses above the long-term moving average and sell signals when the short-term moving average crosses below the long-term moving average.
Step 6: Backtest strategy
Next, we need to backtest our strategy to see how well it performs. We can do this by simulating trades based on our signals and calculating the profit and loss.
“`python
data[‘Profit’] = data[‘Position’] * data[‘Close’].diff()
data[‘Cumulative Profit’] = data[‘Profit’].cumsum()
“`
In this example, we are calculating the profit by multiplying the position (buy or sell) by the change in price between two periods. We then calculate the cumulative profit by summing up the profits over time.
Step 7: Optimize parameters
Finally, we need to optimize our strategy by testing different parameters, such as the length of the moving averages and the stop loss and take profit levels.
We can do this by creating a loop that tests different parameter values and calculates the profit and loss for each combination.
“`python
for short_ma_length in range(10, 30):
for long_ma_length in range(40, 60):
short_ma = data[‘Close’].rolling(window=short_ma_length).mean()
long_ma = data[‘Close’].rolling(window=long_ma_length).mean()
data[‘Signal’] = 0
data[‘Signal’][short_ma_length:] = np.where(short_ma[short_ma_length:] > long_ma[short_ma_length:], 1, 0)
data[‘Position’] = data[‘Signal’].diff()
data[‘Profit’] = data[‘Position’] * data[‘Close’].diff()
data[‘Cumulative Profit’] = data[‘Profit’].cumsum()
results.append((short_ma_length, long_ma_length, data[‘Cumulative Profit’].iloc[-1]))
“`
In this example, we are testing different combinations of short-term and long-term moving average lengths and recording the final cumulative profit for each combination.
Conclusion
In this article, we have explained how to build a forex robot using the moving average crossover strategy in Python. By following these steps, you can create your own automated trading tool and optimize it to suit your trading style and preferences.
It is important to remember that building a forex robot requires a good understanding of trading and programming concepts, and it is crucial to thoroughly backtest and optimize your strategy before using it in live trading.
