Hey guys! Ever wondered if you could predict stock prices in the Indonesian market (IDX) using your own code? Well, you're in luck! This guide will walk you through how to create your own DIY stock price prediction model using Python. We'll cover everything from getting the data to building and evaluating your model. Let's dive in!

Why DIY Stock Price Prediction?

DIY stock price prediction offers several compelling advantages. First off, it gives you complete control over the entire process. You get to choose the data sources, the model architecture, and the evaluation metrics. This level of customization is invaluable because it allows you to tailor the model to your specific needs and investment strategies.

Secondly, building your own model enhances your understanding of the stock market and the factors that influence stock prices. By working hands-on with the data, you'll gain insights into market trends, volatility, and the relationships between different economic indicators. This knowledge can significantly improve your investment decision-making. Moreover, it's a fantastic learning experience. You'll deepen your skills in data analysis, machine learning, and programming, which are highly valuable in today's data-driven world.

Another key benefit is cost-effectiveness. Commercial stock prediction tools can be quite expensive, whereas building your own model can save you a significant amount of money. You'll primarily be using open-source tools and free data sources, making it an accessible option for individual investors and enthusiasts. Finally, the satisfaction of creating something from scratch and seeing it perform is incredibly rewarding. It empowers you to take control of your financial future and make informed decisions based on your own analysis.

Prerequisites

Before we start, make sure you have the following:

• Python: You'll need Python installed on your machine. If you don't have it, download it from the official Python website.
• Libraries: We'll be using several Python libraries, including:
  • yfinance: For fetching stock data.
  • pandas: For data manipulation.
  • numpy: For numerical computations.
  • scikit-learn: For building the prediction model.
  • matplotlib: For data visualization.

Install these libraries using pip:

pip install yfinance pandas numpy scikit-learn matplotlib

Step 1: Gathering Stock Data

Gathering stock data is the initial and crucial step in building a stock price prediction model. High-quality data forms the foundation upon which your model will learn and make predictions. For the Indonesian stock market (IDX), you can use the yfinance library to fetch historical stock data. This library provides a convenient way to access data from Yahoo Finance, which includes a wide range of Indonesian stocks.

To begin, you need to identify the stock tickers you want to analyze. For example, let's consider Telkom Indonesia (TLKM.JK). You can fetch its historical data using the following code:

import yfinance as yf

ticker = "TLKM.JK"  # Telkom Indonesia
data = yf.download(ticker, start="2020-01-01", end="2023-12-31")
print(data.head())

This code snippet downloads the stock data for Telkom Indonesia from January 1, 2020, to December 31, 2023. The data variable will contain a Pandas DataFrame with columns such as 'Open', 'High', 'Low', 'Close', 'Adj Close', and 'Volume'. Ensure that the data range you select is appropriate for your analysis; a longer historical period generally provides more information for the model to learn from.

Once you have the data, it's essential to inspect it for any missing values or inconsistencies. Missing data points can skew your model's performance, so you'll need to handle them appropriately. You can use Pandas functions like data.isnull().sum() to identify missing values and data.dropna() or data.fillna() to handle them. Additionally, check for any outliers or unusual patterns that might require further investigation. Cleaning and preprocessing the data at this stage will significantly improve the accuracy and reliability of your stock price predictions.

Step 2: Preparing the Data

Preparing the data is a critical step in the stock price prediction process. Raw stock data, as obtained from sources like yfinance, typically requires cleaning, transformation, and feature engineering before it can be effectively used to train a predictive model. This preparation ensures that the model receives relevant and well-structured inputs, leading to more accurate and reliable predictions.

First, you need to handle any missing values in the dataset. Missing data can arise due to various reasons, such as trading holidays or data collection errors. Common strategies for dealing with missing values include imputation (filling in the gaps with estimated values) or removal of rows with missing data. For example, you can use the fillna() method in Pandas to replace missing values with the mean or median of the column:

data.fillna(data.mean(), inplace=True)

Next, feature engineering involves creating new features from the existing data that may be more informative for the model. Common features include moving averages, which smooth out short-term fluctuations and highlight longer-term trends, and relative strength index (RSI), which measures the magnitude of recent price changes to evaluate overbought or oversold conditions in the market. For instance, you can calculate a simple moving average (SMA) over a 50-day period:

data['SMA_50'] = data['Close'].rolling(window=50).mean()

Another important transformation is scaling the data. Many machine learning algorithms perform better when the input features are on a similar scale. Techniques like Min-Max scaling or Standardization can be used to bring all features into a comparable range. For example, using Min-Max scaling:

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
data[['Close', 'Volume', 'SMA_50']] = scaler.fit_transform(data[['Close', 'Volume', 'SMA_50']])

Finally, you need to split the data into training and testing sets. The training set is used to train the model, while the testing set is used to evaluate its performance on unseen data. A common split ratio is 80% for training and 20% for testing:

train_data = data[:-int(0.2*len(data))]
test_data = data[-int(0.2*len(data)):]

By carefully preparing the data, you ensure that your model has the best possible chance of learning meaningful patterns and making accurate predictions.

Step 3: Building the Prediction Model

Building the prediction model is where the magic happens. You'll use the prepared data to train a machine learning model that can predict future stock prices. There are several models you can choose from, but for simplicity, we'll use a linear regression model. Linear regression is a simple yet effective algorithm for predicting a continuous target variable based on a linear relationship with one or more input features.

First, you need to select the features that will be used to train the model. These features should be relevant to predicting stock prices and can include historical prices, volume, and technical indicators. For example, you might choose to use the 'Close' price, 'Volume', and 'SMA_50' (Simple Moving Average) as features. You'll also need to define the target variable, which is the stock price you want to predict (e.g., the 'Close' price of the next day).

Next, you'll create the training and testing datasets. The training dataset will be used to train the model, while the testing dataset will be used to evaluate its performance. It's important to split the data in a way that preserves the temporal order, as stock prices are time-series data. A common approach is to use the first 80% of the data for training and the remaining 20% for testing.

from sklearn.linear_model import LinearRegression

# Select features and target
features = ['Close', 'Volume', 'SMA_50']
target = 'Close'

# Drop rows with NaN values after calculating SMA
train_data = train_data.dropna()
test_data = test_data.dropna()

# Prepare data for the model
X_train = train_data[features]
y_train = train_data[target]
X_test = test_data[features]
y_test = test_data[target]

# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

After training the model, you can use it to make predictions on the testing dataset. The predictions will be compared to the actual stock prices to evaluate the model's performance.

Step 4: Evaluating the Model

Evaluating the model is crucial to understand how well your stock price prediction model performs. It involves comparing the model's predictions to the actual stock prices in the testing dataset and calculating various metrics to quantify the accuracy and reliability of the predictions. A well-evaluated model provides insights into its strengths and weaknesses, helping you make informed decisions about its suitability for real-world applications.

One common metric used for evaluating regression models is the Mean Squared Error (MSE). MSE measures the average squared difference between the predicted and actual values. A lower MSE indicates better performance, as it means the model's predictions are closer to the actual values. Another useful metric is the Root Mean Squared Error (RMSE), which is the square root of the MSE. RMSE is easier to interpret because it is in the same units as the target variable.

from sklearn.metrics import mean_squared_error, r2_score
import numpy as np

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse}")
print(f"Root Mean Squared Error: {rmse}")
print(f"R-squared: {r2}")

In addition to MSE and RMSE, the R-squared (R2) score is another important metric. R2 measures the proportion of the variance in the target variable that is predictable from the input features. It ranges from 0 to 1, with higher values indicating a better fit. An R2 score of 1 means that the model perfectly predicts the target variable, while an R2 score of 0 means that the model does not explain any of the variance in the target variable.

By evaluating these metrics, you can gain a comprehensive understanding of your model's performance and identify areas for improvement. If the model's performance is not satisfactory, you can try different models, feature engineering techniques, or hyperparameter tuning to improve its accuracy and reliability.

Step 5: Improving the Model (Optional)

Improving the model is an iterative process that involves refining your stock price prediction model to achieve better accuracy and reliability. This step is crucial if your initial model evaluation reveals that the model's performance is not satisfactory. There are several techniques you can use to improve your model, including feature engineering, hyperparameter tuning, and trying different models.

Feature engineering involves creating new features from the existing data that may be more informative for the model. For example, you can create technical indicators such as the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), or Bollinger Bands. These indicators can capture different aspects of the stock's price movements and potentially improve the model's ability to predict future prices.

Hyperparameter tuning involves adjusting the parameters of the machine learning algorithm to optimize its performance. For example, if you are using a linear regression model, you can try different regularization parameters to prevent overfitting. Techniques like grid search or random search can be used to systematically explore different combinations of hyperparameters and identify the best values.

Trying different models is another way to improve your stock price prediction model. Linear regression is a simple model, but it may not be suitable for capturing the complex patterns in stock prices. You can try more advanced models such as Support Vector Machines (SVM), Random Forests, or Neural Networks. These models can potentially capture non-linear relationships and improve the model's accuracy.

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor

# Define the parameter grid
param_grid = {
    'n_estimators': [100, 200, 300],
    'max_depth': [5, 10, 15]
}

# Create a Random Forest Regressor
rf = RandomForestRegressor(random_state=42)

# Use GridSearchCV to find the best hyperparameters
grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=3, scoring='neg_mean_squared_error')
grid_search.fit(X_train, y_train)

# Get the best model
best_model = grid_search.best_estimator_

By iteratively refining your model using these techniques, you can improve its accuracy and reliability and make it more suitable for real-world stock price prediction.

Conclusion

So there you have it! Building your own stock price prediction model for the Indonesian market is totally doable. It takes some effort, but the knowledge and control you gain are well worth it. Plus, it's a super cool project to show off your Python skills. Happy coding, and good luck with your stock predictions!