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--- |
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license: unknown |
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datasets: |
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- GeoTrix/Datathon |
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--- |
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# Model For Datathon |
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This repo holds 2 final models that was used and made for Ristek Datathon. The first model is a Linear Regression Model, |
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and the other is an LSTM Model. The datasets use to train the model rely with the yfinance python module, which is a module that was made by |
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Yahoo! that stores the stock market data, from what price it opens, close, the highest price it reach on that day and the lowest and the volume. |
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It was also slighly modified with some added features that was used for the Linear Regression Model, adding SMA, EMA and RSI. While the LSTM Model |
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only relied only on the Closing price of the stock and without the added modified dataset due to during testing it generated the best result |
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when just using the close feature. |
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## Using Linear Regression Model |
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Sample python code to see the linear regression model |
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``` |
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from joblib import load |
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import yfinance as yf |
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# Grab the most recent stock data |
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data = yf.download('AAPL', period='40d') |
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# Adding SMA (Simple Moving Average) into the data |
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data['SMA_5'] = data['Close'].rolling(window=5).mean() |
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data['SMA_10'] = data['Close'].rolling(window=10).mean() |
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# Adding EMA (Exponential Moving Average) into the data |
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data['EMA_5'] = data['Close'].ewm(span=5, adjust=False).mean() |
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data['EMA_10'] = data['Close'].ewm(span=10, adjust=False).mean() |
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# Adding RSI (Relative Strength Index) into the data |
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delta = data['Close'].diff() |
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avg_gain = (delta.where(delta > 0, 0)).rolling(window=14).mean() |
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avg_loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean() |
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rs = avg_gain / avg_loss |
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data['RSI'] = 100 - (100 / (1 + rs)) |
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# Grab the features of the final stock price to predict the next day stock |
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feature = data[['Open', 'High', 'Low', 'Close', 'Volume', 'SMA_5', 'SMA_10', 'EMA_5', 'EMA_10', 'RSI']].tail().values |
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# Load the models in your directory |
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model = load('Path/Of/The/Model') |
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predictions = model.predict(feature) |
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# Predicts gives the next day stock |
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print(f"Predicted: ${predictions[0]:.2f}") |
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``` |
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## Using LSTM Model |
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Sample python code to see the LSTM model |
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``` |
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import yfinance as yf |
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from tensorflow.keras.models import load_model |
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from sklearn.preprocessing import MinMaxScaler |
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import numpy as np |
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# Grab the most recent stock data |
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data = yf.download('AAPL', period='40d') |
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# Normalize the data |
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scaler = MinMaxScaler(feature_range=(0, 1)) |
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scaled_features = scaler.fit_transform(data[['Close']]) |
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# Grabbing the last 30 days of data for prediction |
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windows_size = 30 |
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last_30_days = scaled_features[-windows_size:] |
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# Shaping the data to match the model input |
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x = np.reshape(last_30_days, (1, windows_size, 1)) |
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# Loading model |
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model = load_model('Models/Stockly-lstm.keras') |
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# Predicting the next day's stock price |
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predictions = model.predict(x) |
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predictions = scaler.inverse_transform(predictions) |
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print(f"Predicted: ${predictions[0][0]:.2f}") |
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``` |
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**NOTE:** These models are still very new and not that highly accurate. So don't really use them for actual usage to trade in the stock market, |
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but rather have fun with it, since the model itself was made pretty rushed to complete the Datathon. If the model ever get updated to be more accurate |
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this NOTE should be gone. |
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