94 lines
3.7 KiB
Python
94 lines
3.7 KiB
Python
import pandas as pd
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from prophet import Prophet
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from datetime import datetime
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import yfinance as yf
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import numpy as np
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import asyncio
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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#import matplotlib.pyplot as plt
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async def download_data(ticker, start_date, end_date):
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try:
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df = yf.download(ticker, start=start_date, end=end_date, interval="1d")
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df = df.reset_index()
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df = df[['Date', 'Adj Close']]
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df = df.rename(columns={"Date": "ds", "Adj Close": "y"})
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if len(df) > 252*2: #At least 2 years of history is necessary
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#df['y'] = df['y'].rolling(window=200).mean()
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#df = df.dropna()
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return df
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except Exception as e:
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print(e)
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class PricePredictor:
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def __init__(self, predict_ndays=365):
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self.predict_ndays = predict_ndays
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self.model = Prophet(
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interval_width = 0.8,
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daily_seasonality=True,
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yearly_seasonality = True,
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)
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def run(self, df):
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self.model.fit(df)
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future = self.model.make_future_dataframe(periods=self.predict_ndays)
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forecast = self.model.predict(future)
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# Apply rolling average to smooth the upper bound
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rolling_window = 200
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forecast['smoothed_upper'] = round(forecast['yhat_upper'].rolling(window=rolling_window, min_periods=1).mean(),2)
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forecast['smoothed_lower'] = round(forecast['yhat_lower'].rolling(window=rolling_window, min_periods=1).mean(),2)
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forecast['smoothed_mean'] = round(forecast['yhat'].rolling(window=rolling_window, min_periods=1).mean(),2)
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actual_values = df['y'].values
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predicted_values = forecast['yhat'].values[:-self.predict_ndays]
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rmse = round(np.sqrt(mean_squared_error(actual_values, predicted_values)),2)
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mape = round(np.mean(np.abs((actual_values - predicted_values) / actual_values)) * 100)
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r2 = round(r2_score(actual_values, predicted_values)*100)
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print("RMSE:", rmse)
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print("MAPE:", mape)
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print("R2 Score:", r2)
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pred_date_list = forecast['ds'][-1200-self.predict_ndays:].dt.strftime('%Y-%m-%d').tolist()
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upper_list = forecast['smoothed_upper'][-1200-self.predict_ndays:].tolist()
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lower_list = forecast['smoothed_lower'][-1200-self.predict_ndays:].tolist()
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mean_list = forecast['smoothed_mean'][-1200-self.predict_ndays:].tolist()
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historical_date_list = df['ds'][-1200:].dt.strftime('%Y-%m-%d').tolist()
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historical_price_list = round(df['y'][-1200:],2).tolist()
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return {'rmse': rmse,'mape': mape,'r2Score':r2, 'historicalPrice': historical_price_list, 'predictionDate': pred_date_list, 'upperBand': upper_list, 'lowerBand': lower_list, 'meanResult': mean_list}
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#Test Mode
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async def main():
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for ticker in ['TSLA']:
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start_date = datetime(2000, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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df = await download_data(ticker, start_date, end_date)
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data = PricePredictor().run(df)
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# Run the main function
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#asyncio.run(main())
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# Plotting
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'''
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.plot(forecast['ds'][-1200-predict_ndays:], forecast['smoothed_mean'][-1200-predict_ndays:], color='blue', label='Predicted')
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ax.fill_between(forecast['ds'][-1200-predict_ndays:], forecast['smoothed_lower'][-1200-predict_ndays:], forecast['smoothed_upper'][-1200-predict_ndays:], color='gray', alpha=0.5, label='Confidence Interval')
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ax.plot(df['ds'][-1200:], df['y'][-1200:], color='black', label='Actual')
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ax.set_xlabel('Date')
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ax.set_ylabel('Price')
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ax.set_title('Forecasted Prices for {}'.format(ticker))
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ax.legend()
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ax.grid(True)
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plt.show()
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''' |