Programming an Intelligent Forex Trading Algorithm

Luiggi Trejo
4 min readOct 23, 2023
Photo by Aideal Hwa on Unsplash

Quantitative analysis lies at the very heart of algorithmic trading, serving as the foundational methodology that empowers traders to create and execute strategies with a high degree of precision.

This approach involves the use of historical data, statistical models, and mathematical algorithms to inform trading decisions, leading to several advantages and nuanced insights that contribute to the success of algorithmic trading strategies.

Here, my dear reader, you have a program that reads financial data for EUR/USD, calculates the probability of the price going up or down, and opens buy or sell positions based on these probabilities:

# Import necessary libraries
import historical_data_library
import machine_learning_library
import trading_library

# Define parameters
lookback_period = 30 # Number of days to look back for historical data
data = historical_data_library.get_historical_data("EUR/USD", lookback_period)
features = machine_learning_library.extract_features(data)

# Train a machine learning model to predict price movements
model = machine_learning_library.train_model(features)

# Define a function to calculate probabilities
def calculate_probabilities(model, data_point):
probability_up = model.predict_probability_up(data_point)
probability_down = 1 - probability_up
return…

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