Introduction

Artificial Intelligence (AI) has revolutionized trading by providing advanced tools to analyze large datasets and make predictions. This project demonstrates how to build a simple AI model for trading using historical price data.

Getting Started

These instructions will help you set up and run the AI trading model on your local machine.

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• Jupyter Notebook (optional, for interactive development)

Installation

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Data Preparation

1. Obtain Historical Data:
   Download historical trading data from a reliable source (e.g., Yahoo Finance, Alpha Vantage).

2. Data Preprocessing:
   Clean and preprocess the data to remove any inconsistencies. Typical preprocessing steps include handling missing values, normalizing data, and feature engineering.

Example preprocessing script:

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
 
# Load data
data = pd.read_csv('historical_data.csv')
 
# Handle missing values
data = data.dropna()
 
# Normalize data
scaler = MinMaxScaler()
data[['Open', 'High', 'Low', 'Close', 'Volume']] = scaler.fit_transform(data[['Open', 'High', 'Low', 'Close', 'Volume']])
 
# Save preprocessed data
data.to_csv('preprocessed_data.csv', index=False)

Model Building

1. Define the Model:
   Choose a machine learning algorithm suitable for time series prediction. Common choices include LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) networks.

Example model definition:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
 
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=1))
 
model.compile(optimizer='adam', loss='mean_squared_error')

Training the Model

1. Split the Data:
   Split the data into training and testing sets.

from sklearn.model_selection import train_test_split
 
X = data[['Open', 'High', 'Low', 'Close', 'Volume']].values
y = data['Close'].values
 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

2. Train the Model:
   Fit the model to the training data.

model.fit(X_train, y_train, epochs=50, batch_size=32)

Evaluating the Model

1. Evaluate Performance:
   Use appropriate metrics to evaluate the model's performance on the test data.

from sklearn.metrics import mean_squared_error
 
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse}')

Making Predictions

1. Make Predictions:
   Use the trained model to make predictions on new data.

new_data = pd.read_csv('new_data.csv')
new_data_scaled = scaler.transform(new_data)
predictions = model.predict(new_data_scaled)
print(predictions)

Conclusion

This project demonstrates how to build and evaluate an AI model for trading. By following the steps outlined in this README, you can create your own model to analyze and predict trading data.