91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
from datetime import datetime, timedelta
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import os
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import sys
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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():
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# Get the Symbol from ARGV
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Symbol = sys.argv[1]
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# get the number of days ago to run the simulation for
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DaysBack = int(sys.argv[2])
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print(f"Days back: {DaysBack}")
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# calculate the time offsets
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end_date = datetime.now() - timedelta(days=DaysBack)
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start_date = end_date - timedelta(days=70)
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# convert to string formats
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start_str = start_date.strftime('%Y-%m-%d')
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end_str = end_date.strftime('%Y-%m-%d')
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print(f"Start Date: {start_str}")
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print(f"End Date: {end_str}")
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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, start=start_str, end=end_str, auto_adjust=True, progress=False)
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if not df.empty:
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# Remove the horizontal ticker column
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df.columns = df.columns.get_level_values(0)
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# Add in the Vertical ticker column
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df['Ticker'] = Symbol
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# Make the feature set
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df = features.MakeFeatures(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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# 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.reshape(-1, 1)).flatten()
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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
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# Get the overall trend to pull predictions from
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predictionTrend = 0
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with open("Target_Close_Average.txt", "r") as f:
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predictionTrend = float(f.read().strip())
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# Set the movement indicator
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movement_indicator = 0
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averagePrediction = np.mean(flat_predictions) + predictionTrend
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# Return to C# via stdout
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print(f"---RESULT_START---")
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print(averagePrediction)
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print(f"---RESULT_END---")
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if __name__ == "__main__":
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Predict() |