derek holloway
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import os
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import json
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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():
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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("SPY", 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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# 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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# Predict
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predictions = reconstructed_model.predict(df)
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# 'predictions' will be a 2D array, flatten it if you want a simple list
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flat_predictions = predictions.flatten().tolist()
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print(f"Predicted Target_Close: {flat_predictions}")
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return json.dumps(flat_predictions) |