derek holloway
66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
import os
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import json
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import joblib
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import numpy as np
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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import yfinance as yf
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import features
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import matplotlib
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matplotlib.use("Agg")
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def Predict(Symbol):
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# Define paths (consistent with your previous script)
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(SCRIPT_DIR, "data")
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MODEL_PATH = os.path.join(DATA_DIR, "model.keras")
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# Pull 1 month of current data to make prediction against | for volatility 20
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df = yf.download(Symbol, period="2mo", auto_adjust=True)
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if not df.empty:
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df = features.MakeFeatures(df, 1)
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df = features.CleanDF(df)
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print(Symbol)
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# Drop our predictor
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df.drop('Target_Close', axis=1, inplace=True)
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# Lazy load this so it doesnt interfere with yfinance
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from keras.models import load_model
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# Load the model
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reconstructed_model = load_model(MODEL_PATH)
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# Verify it loaded correctly
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reconstructed_model.summary()
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# Load the scalers
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feature_scaler = joblib.load(os.path.join(DATA_DIR, "feature_scaler.pkl"))
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target_scaler = joblib.load(os.path.join(DATA_DIR, "target_scaler.pkl"))
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# Scale the data
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scaled_data = feature_scaler.transform(df)
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# Predict
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scaled_predictions = reconstructed_model.predict(scaled_data)
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# Use the loaded target scaler to get back to % change
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actual_prediction = target_scaler.inverse_transform(scaled_predictions)
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# 'predictions' will be a 2D array, flatten it if you want a simple list
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flat_predictions = actual_prediction.flatten().tolist()
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print(f"Predicted Target_Close: {flat_predictions}")
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movement_indicator = 0
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if (np.mean(flat_predictions) > 0.01):
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movement_indicator = 1
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elif (np.mean(flat_predictions) < -0.01):
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movement_indicator = -1
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else:
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movement_indicator = 0
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return movement_indicator
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if __name__ == "__main__":
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Predict() |