File size: 2,064 Bytes
c3bf538 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import yfinance as yf
from prophet import Prophet
import pandas as pd
from typing import Dict, Any
def generate_forecast(ticker: str) -> Dict[str, Any]:
print(f"Generating forecast for ticker {ticker}...")
stock_data = yf.download(ticker, period="2y", progress=False)
if stock_data.empty:
return {"error": f"Could not download historical data for {ticker}."}
df_prophet = stock_data[['Close']].copy()
df_prophet.reset_index(inplace=True)
# 3. Rename the columns to what Prophet expects.
df_prophet.columns = ['ds', 'y']
model = Prophet(
daily_seasonality=False,
weekly_seasonality=True,
yearly_seasonality=True,
changepoint_prior_scale=0.05
)
model.fit(df_prophet)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)
current_price = df_prophet['y'].iloc[-1]
predicted_price_30_days = forecast['yhat'].iloc[-1]
trend = "upward" if predicted_price_30_days > current_price else "downward"
change_percent = ((predicted_price_30_days - current_price) / current_price) * 100
forecast_data = {
"summary": (
f"The model predicts a {trend} trend over the next 30 days. "
f"Current price: {current_price:.2f}, "
f"predicted price in 30 days: {predicted_price_30_days:.2f} "
f"({change_percent:+.2f}% change)."
),
# Convert datetime objects to strings for JSON compatibility
"history_plot_data": [
{'ds': r['ds'].isoformat(), 'y': r['y']} for r in df_prophet.tail(90).to_dict('records')
],
"forecast_plot_data": [
{
'ds': r['ds'].isoformat(),
'yhat': r['yhat'],
'yhat_lower': r['yhat_lower'],
'yhat_upper': r['yhat_upper']
} for r in forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(120).to_dict('records')
]
}
print(f"Forecast for {ticker} generated successfully.")
return forecast_data |